├── images ├── gds.jpg └── plugin.jpg ├── .gitignore ├── nxneo4j ├── graph.py ├── di_graph.py ├── exceptions.py ├── __init__.py ├── centrality.py ├── path_finding.py ├── community.py ├── draw.py └── base_graph.py ├── test.py ├── setup.py ├── CHANGELOG.md ├── README.adoc ├── LICENSE └── examples ├── nxneo4j_tutorial_latest.ipynb ├── vis.html └── nxneo4j 0.0.2 tutorial.ipynb /images/gds.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jbaktir/networkx-neo4j/HEAD/images/gds.jpg -------------------------------------------------------------------------------- /images/plugin.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jbaktir/networkx-neo4j/HEAD/images/plugin.jpg -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Created by .ignore support plugin (hsz.mobi) 2 | __pycache__ 3 | .ipynb_checkpoints 4 | .DS_Store 5 | -------------------------------------------------------------------------------- /nxneo4j/graph.py: -------------------------------------------------------------------------------- 1 | from nxneo4j.base_graph import BaseGraph 2 | 3 | 4 | class Graph(BaseGraph): 5 | def __init__(self, driver, config=None): 6 | super().__init__(driver, "UNDIRECTED", config) 7 | -------------------------------------------------------------------------------- /nxneo4j/di_graph.py: -------------------------------------------------------------------------------- 1 | from nxneo4j.base_graph import BaseGraph 2 | 3 | 4 | class DiGraph(BaseGraph): 5 | def __init__(self, driver, config=None): 6 | super().__init__(driver, "NATURAL", config) 7 | -------------------------------------------------------------------------------- /nxneo4j/exceptions.py: -------------------------------------------------------------------------------- 1 | class NetworkXException(Exception): 2 | """Base class for exceptions in NetworkX.""" 3 | 4 | class NetworkXError(NetworkXException): 5 | """Exception for a serious error in NetworkX""" 6 | -------------------------------------------------------------------------------- /nxneo4j/__init__.py: -------------------------------------------------------------------------------- 1 | from nxneo4j.centrality import * 2 | from nxneo4j.community import * 3 | from nxneo4j.path_finding import * 4 | from nxneo4j.graph import Graph 5 | from nxneo4j.di_graph import DiGraph 6 | from nxneo4j.draw import * 7 | from nxneo4j.exceptions import * 8 | -------------------------------------------------------------------------------- /test.py: -------------------------------------------------------------------------------- 1 | # test only (import sys;sys.path.append("../")) #the purpose is to reach to the parent directory 2 | # to fix the default port run $kill $(lsof -ti:7687) 3 | """ 4 | known fails 5 | G.add_node("Betul",age=4) 6 | G.add_node("Betul",age=5) #this does not update the first one 7 | 8 | G.nodes['Betul']['age'] = 5 #also does not work 9 | 10 | list(G.edges(data=True)) it would be nice to display labels here 11 | 12 | G.edges(['Betul','Nurgul']) #FAILS 13 | 14 | 15 | """ 16 | 17 | 18 | from neo4j import GraphDatabase 19 | import nxneo4j as nx 20 | 21 | driver = GraphDatabase.driver(uri="bolt://localhost:11003",auth=("neo4j","neo")) 22 | G = nx.DiGraph(driver) 23 | 24 | 25 | G.add_node(1) 26 | G.add_nodes_from([2,3,4]) 27 | G.add_edge(1,2) 28 | G.add_edges_from([(2,3),(3,4)]) 29 | 30 | nx.betweenness_centrality(G) 31 | nx.closeness_centrality(G) 32 | nx.pagerank(G) 33 | nx.triangles(G) 34 | nx.clustering(G) 35 | list(nx.community.label_propagation_communities(G)) 36 | nx.shortest_path(G, source=1, target=4) 37 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | """ 2 | Setup script for networkx-neo4j 3 | You can install networkx-neo4j with 4 | python setup.py install 5 | """ 6 | import os 7 | import sys 8 | 9 | if os.path.exists('MANIFEST'): 10 | os.remove('MANIFEST') 11 | 12 | from setuptools import setup 13 | 14 | if sys.argv[-1] == 'setup.py': 15 | print("To install, run 'python setup.py install'") 16 | print() 17 | 18 | if sys.version_info[:2] < (3, 6): 19 | print("NetworkX Neo4j requires Python 3.6 or later (%d.%d detected)." % 20 | sys.version_info[:2]) 21 | sys.exit(-1) 22 | 23 | packages = [ 24 | "nxneo4j", 25 | ] 26 | 27 | if __name__ == "__main__": 28 | setup( 29 | name="networkx-neo4j", 30 | version="0.0.3", 31 | maintainer="Mark Needham", 32 | maintainer_email="m.h.needham@gmal.com", 33 | author="Mark Needham", 34 | author_email="m.h.needham@gmail.com", 35 | description="NetworkX API for Neo4j Graph Algorithms", 36 | keywords="neo4j, networkx", 37 | long_description="NetworkX API for Neo4j Graph Algorithms", 38 | license="Apache 2", 39 | platforms="All", 40 | url="http://markhneedham.com", 41 | install_requires=[ 42 | 'neo4j-driver', 43 | ], 44 | packages=packages, 45 | zip_safe=False 46 | ) 47 | -------------------------------------------------------------------------------- /CHANGELOG.md: -------------------------------------------------------------------------------- 1 | # Changelog 2 | All notable changes to this project will be documented in this file. 3 | 4 | The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), 5 | and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). 6 | 7 | ## [0.0.3] - [Unreleased] 8 | ### Added (by [@ybaktir](https://github.com/ybaktir)) 9 | - CHANGELOG.md 10 | - nx.draw(G) 11 | - G.nodes 12 | - G.edges 13 | - G.clear() to delete all the data, acts similar to G.delete_all() 14 | - len(G) to display number of nodes 15 | - G.remove_node() 16 | 17 | ### Changed (by [@ybaktir](https://github.com/ybaktir)) 18 | - reorganized the entire file structure to better separate the algorithms files from the graph files. 19 | - changed README.md with new figures, dependencies 20 | - all the $nodeLabel, $relationshipType params changed to $node_label and $relationship_type for better code readibility 21 | - updated G.load_got() to fix constraint errors 22 | - updated G.load_euroads() to fix constraint errors 23 | - updated G.load_twitter() to fix constraint errors 24 | - updated G.add_edge() to enable property assignment 25 | - updated G.add_node() to enable property assignment 26 | 27 | 28 | ### Removed 29 | - No removals 30 | 31 | ### Known Issues (by [@ybaktir](https://github.com/ybaktir)) 32 | - len(G) doesn't return the correct value, uses config restrictions 33 | - after G.load_got() and after nx.draw(G), some of the relationship labels don't show on the visualization 34 | 35 | ## [0.0.2] - 2020-08-25 36 | ### Added (by [@ybaktir](https://github.com/ybaktir)) 37 | - G.delete_all() 38 | - G.load_got() 39 | - G.load_euroads() 40 | - G.load_twitter() 41 | 42 | ### Changed (by [@ybaktir](https://github.com/ybaktir)) 43 | - All "apoc" based code is moved to Graph Data Science library aka "gds" since apoc is not supported by neo4j 4.x. 44 | - All {params} syntax moved to $params syntax since {params} no longer supported. 45 | - Updated README.md. 46 | - Updated the examples file. 47 | 48 | ### Removed (by [@ybaktir](https://github.com/ybaktir)) 49 | - Removed functionality of harmonic centrality, average clustering as they are not supported by gds. 50 | - Removed test file as it was hard to maintain the code because of too many changes. The file will back in the future versions. 51 | -------------------------------------------------------------------------------- /README.adoc: -------------------------------------------------------------------------------- 1 | = networkx-neo4j 2 | 3 | This library provides NetworkX API for https://github.com/neo4j/graph-data-science/[Neo4j Graph Data Science^]. 4 | You should be able to use it as you would NetworkX but algorithms will run against Neo4j. 5 | 6 | == Dependencies 7 | 8 | - ≥ Neo4j 4.x 9 | - Graph Data Science Library Plugin 10 | - APOC Plugin 11 | - ≥ Python 3.6 12 | - ≥ neo4j-driver 4.x 13 | 14 | == Installation 15 | 16 | You can install the library by running the following command: 17 | 18 | [source, bash] 19 | ---- 20 | pip install networkx-neo4j 21 | ---- 22 | 23 | 24 | You'll also need to install APOC and the Graph Algorithms library. 25 | 26 | image:images/plugin.jpg[plugin] 27 | image:images/gds.jpg[gds] 28 | 29 | == Usage 30 | 31 | Here's how you use it. 32 | 33 | First let's import our libraries and create an instance of the Neo4j driver: 34 | 35 | [source, python] 36 | ---- 37 | >>> from neo4j import GraphDatabase 38 | >>> import nxneo4j as nx 39 | 40 | >>> driver = GraphDatabase.driver(uri="bolt://localhost",auth=("neo4j","your_password")) 41 | ---- 42 | For undirected Graphs: 43 | [source, python] 44 | ---- 45 | >>> G = nx.Graph(driver) 46 | ---- 47 | For directed Graphs: 48 | [source, python] 49 | ---- 50 | >>> G = nx.DiGraph(driver) 51 | ---- 52 | 53 | The available functions in `nxneo4j` are: 54 | [source, python] 55 | ---- 56 | # ADD ONE NODE 57 | G.add_node(node) 58 | node: str, int 59 | >>> G.add_node(1) 60 | 61 | # ADD MULTIPLE NODES 62 | G.add_nodes_from(value) 63 | values: list 64 | >>> G.add_nodes_from([1, 2, 3, 4]) 65 | 66 | # ADD ONE EDGE 67 | G.add_edge(node1,node2) 68 | node1: str, int 69 | node2: str, int 70 | >>> G.add_edge(1,2) 71 | 72 | #ADD MULTIPLE EDGES 73 | G.add_edges_from(values) 74 | values: list of tuples 75 | >>> G.add_edges_from([(1, 2),(2, 3),(3, 4)]) 76 | ---- 77 | 78 | The available algoritms in `nxneo4j` are: 79 | [source, python] 80 | ---- 81 | >>> nx.betweenness_centrality(G) 82 | {3: 4.0, 4: 3.0, 1: 0.0, 2: 0.0, 5: 0.0} 83 | 84 | >>> nx.closeness_centrality(G) 85 | {3: 0.8, 4: 0.6666666666666666, 1: 0.5714285714285714, 2: 0.5714285714285714, 5: 0.4444444444444444} 86 | 87 | >>> nx.pagerank(G) 88 | {3: 1.4170146573314513, 4: 1.0629939728840803, 1: 0.9591085771210682, 2: 0.9591085771210682, 5: 0.6017724112363687} 89 | 90 | >>> nx.triangles(G) 91 | {1: 1, 2: 1, 3: 1, 4: 0, 5: 0} 92 | 93 | >>> nx.clustering(G) 94 | {1: 1.0, 2: 1.0, 3: 0.3333333333333333, 4: 0.0, 5: 0.0} 95 | 96 | >>> list(nx.community.label_propagation_communities(G)) 97 | [{1, 2, 3, 4, 5}] 98 | 99 | >>> nx.shortest_path(G, source=1, target=5) 100 | [1, 3, 4, 5] 101 | 102 | ---- 103 | 104 | == Credits 105 | Yusuf Baktir 106 | Mark Needham 107 | David Jablonski 108 | -------------------------------------------------------------------------------- /nxneo4j/centrality.py: -------------------------------------------------------------------------------- 1 | def betweenness_centrality(G, k=None, normalized=True, weight=None, endpoints=False, seed=None): 2 | # doesn't currently support `weight`, `k`, `endpoints`, `seed` 3 | 4 | query = """\ 5 | CALL gds.betweenness.stream({ 6 | nodeProjection: $node_label, 7 | relationshipProjection: { 8 | relType: { 9 | type: $relationship_type, 10 | orientation: $direction, 11 | properties: {} 12 | } 13 | } 14 | }) 15 | YIELD nodeId, score 16 | RETURN gds.util.asNode(nodeId).%s AS node, score 17 | ORDER BY node ASC 18 | """ % G.identifier_property 19 | 20 | params = G.base_params() 21 | 22 | with G.driver.session() as session: 23 | result = {row["node"]: row["score"] for row in session.run(query, params)} 24 | return result 25 | 26 | 27 | 28 | def closeness_centrality(G, u=None, distance=None, wf_improved=True, reverse=False): 29 | # doesn't currently supported `distance`, `reverse`, `wf_improved` 30 | 31 | query = """\ 32 | CALL gds.alpha.closeness.stream({ 33 | nodeProjection: $node_label, 34 | relationshipProjection: { 35 | relType: { 36 | type: $relationship_type, 37 | orientation: $direction, 38 | properties: {} 39 | } 40 | }}) 41 | YIELD nodeId, centrality 42 | RETURN gds.util.asNode(nodeId).%s AS node, centrality 43 | ORDER BY node ASC 44 | """ % G.identifier_property 45 | 46 | params = G.base_params() 47 | 48 | with G.driver.session() as session: 49 | result = {row["node"]: row["centrality"] for row in session.run(query, params)} 50 | if u: 51 | return result[u] 52 | return result 53 | 54 | 55 | 56 | def pagerank(G, alpha=0.85, personalization=None, max_iter=100, tol=1.0e-8, nstart=None, weight='weight'): 57 | # doesn't currently supported `personalization`, `tol`, `nstart`, `weight` 58 | 59 | query = """\ 60 | CALL gds.pageRank.stream({ 61 | nodeProjection: $node_label, 62 | relationshipProjection: { 63 | relType: { 64 | type: $relationship_type, 65 | orientation: $direction, 66 | properties: {} 67 | } 68 | }, 69 | relationshipWeightProperty: null, 70 | dampingFactor: $dampingFactor, 71 | maxIterations: $iterations 72 | }) YIELD nodeId, score 73 | WITH gds.util.asNode(nodeId).%s AS node, score 74 | RETURN node, score 75 | """ % G.identifier_property 76 | 77 | 78 | params = G.base_params() 79 | params["iterations"] = max_iter 80 | params["dampingFactor"] = alpha 81 | 82 | with G.driver.session() as session: 83 | result = {row["node"]: row["score"] for row in session.run(query, params)} 84 | return result 85 | -------------------------------------------------------------------------------- /nxneo4j/path_finding.py: -------------------------------------------------------------------------------- 1 | def shortest_weighted_path(G,source, target, weight): 2 | if source is None: 3 | if target is None: 4 | # Find paths between all pairs. 5 | if weight is None: 6 | # paths = dict(nx.all_pairs_shortest_path(G)) 7 | paths = {} 8 | else: 9 | # paths = dict(nx.all_pairs_dijkstra_path(G, weight=weight)) 10 | paths = {} 11 | else: 12 | # Find paths from all nodes co-accessible to the target. 13 | if weight is None: 14 | # paths = nx.single_source_shortest_path(G, target) 15 | # paths = G.single_source_shortest_path(target) 16 | paths = [] 17 | else: 18 | # paths = nx.single_source_dijkstra_path(G, target, 19 | # weight=weight) 20 | paths = [] 21 | else: 22 | if target is None: 23 | # Find paths to all nodes accessible from the source. 24 | if weight is None: 25 | # paths = nx.single_source_shortest_path(G, source) 26 | paths = [] 27 | # paths = G.single_source_shortest_path(source) 28 | else: 29 | # paths = nx.single_source_dijkstra_path(G, source, 30 | # weight=weight) 31 | paths = [] 32 | else: 33 | 34 | query = """\ 35 | MATCH (source:%s {%s: $source }) 36 | MATCH (target:%s {%s: $target }) 37 | 38 | CALL gds.alpha.shortestPath.stream({ 39 | nodeProjection: $node_label, 40 | relationshipProjection: { 41 | relType: { 42 | type: $relationship_type, 43 | orientation: $direction, 44 | properties: {} 45 | } 46 | }, 47 | startNode: source, 48 | endNode: target, 49 | relationshipWeightProperty: $weight, 50 | relationshipProperties: [$weight] 51 | }) 52 | YIELD nodeId, cost 53 | RETURN gds.util.asNode(nodeId).%s AS node, cost 54 | """ % ( 55 | G.node_label, 56 | G.identifier_property, 57 | G.node_label, 58 | G.identifier_property, 59 | G.identifier_property 60 | ) 61 | 62 | with G.driver.session() as session: 63 | params = G.base_params() 64 | params["source"] = source 65 | params["target"] = target 66 | params["weight"] = weight 67 | 68 | paths = [row["node"] for row in session.run(query, params)] 69 | return paths 70 | 71 | def shortest_path(G,source, target): 72 | return shortest_weighted_path(G,source, target, weight='') 73 | -------------------------------------------------------------------------------- /nxneo4j/community.py: -------------------------------------------------------------------------------- 1 | def triangles(G, nodes=None): 2 | query = """\ 3 | CALL gds.triangleCount.stream({ 4 | nodeProjection: $node_label, 5 | relationshipProjection: { 6 | relType: { 7 | type: $relationship_type, 8 | orientation: $direction, 9 | properties: {} 10 | } 11 | }}) 12 | YIELD nodeId, triangleCount 13 | RETURN gds.util.asNode(nodeId).%s AS node, triangleCount 14 | ORDER BY node ASC""" % G.identifier_property 15 | 16 | params = G.base_params() 17 | 18 | with G.driver.session() as session: 19 | result = {row["node"]: row["triangleCount"] for row in session.run(query, params)} 20 | 21 | if nodes: 22 | return {k: v for k, v in result.items() if k in nodes} 23 | return result 24 | 25 | def clustering(G, nodes=None, weight=None): 26 | # doesn't currently support `weight` 27 | query = """\ 28 | CALL gds.localClusteringCoefficient.stream({ 29 | nodeProjection: $node_label, 30 | relationshipProjection: { 31 | relType: { 32 | type: $relationship_type, 33 | orientation: $direction, 34 | properties: {} 35 | } 36 | }}) 37 | YIELD nodeId, localClusteringCoefficient 38 | RETURN gds.util.asNode(nodeId).%s AS node, localClusteringCoefficient 39 | ORDER BY localClusteringCoefficient DESC""" % G.identifier_property 40 | 41 | params = G.base_params() 42 | 43 | with G.driver.session() as session: 44 | result = {row["node"]: row["localClusteringCoefficient"] for row in session.run(query, params)} 45 | return result 46 | 47 | def label_propagation_communities(G): 48 | 49 | query = """\ 50 | CALL gds.labelPropagation.stream({ 51 | nodeProjection: $node_label, 52 | relationshipProjection: { 53 | relType: { 54 | type: $relationship_type, 55 | orientation: $direction, 56 | properties: {} 57 | } 58 | }, 59 | relationshipWeightProperty: null 60 | }) 61 | YIELD nodeId, communityId AS community 62 | MATCH (n) WHERE id(n) = nodeId 63 | RETURN community, collect(n.%s) AS nodes 64 | """ % G.identifier_property 65 | 66 | params = G.base_params() 67 | 68 | with G.driver.session() as session: 69 | for row in session.run(query, params): 70 | yield set(row["nodes"]) 71 | 72 | def connected_components(G): 73 | 74 | query = """\ 75 | CALL gds.wcc.stream({ 76 | nodeProjection: $node_label, 77 | relationshipProjection: { 78 | relType: { 79 | type: $relationship_type, 80 | orientation: $direction, 81 | properties: {} 82 | } 83 | } 84 | }) 85 | YIELD nodeId, componentId AS community 86 | MATCH (n) WHERE id(n) = nodeId 87 | RETURN community, collect(n.%s) AS nodes 88 | ORDER BY community DESC 89 | """ % G.identifier_property 90 | 91 | params = G.base_params() 92 | 93 | with G.driver.session() as session: 94 | for row in session.run(query, params): 95 | yield set(row["nodes"]) 96 | 97 | def number_connected_components(G): 98 | return sum(1 for cc in connected_components(G)) 99 | -------------------------------------------------------------------------------- /nxneo4j/draw.py: -------------------------------------------------------------------------------- 1 | from IPython.display import IFrame 2 | 3 | def draw(G, limit=100): 4 | query = f""" 5 | MATCH (n) 6 | WITH n, rand() AS random 7 | ORDER BY random 8 | LIMIT {limit} 9 | OPTIONAL MATCH (n)-[r]->(m) 10 | RETURN n.{G.identifier_property} AS source_node, 11 | id(n) AS source_id, 12 | type(r) as label, 13 | m.{G.identifier_property} AS target_node, 14 | id(m) AS target_id 15 | """ 16 | 17 | result = G.driver.session().run(query) 18 | nodes = [] 19 | edges = [] 20 | for row in result: 21 | node1 = {'id':row['source_id'],'label':str(row['source_node'])} 22 | node2 = {'id':row['target_id'],'label':str(row['target_node'])} 23 | edge = {'from':row['source_id'],'to':row['target_id'],'label':row['label']} 24 | if (node1 not in nodes) & (node1['id'] != None): 25 | nodes.append(node1) 26 | if (node2 not in nodes) & (node2['id'] != None): 27 | nodes.append(node2) 28 | if (edge not in edges) & (edge['to'] != None): 29 | edges.append(edge) 30 | 31 | # 32 | html = f""" 33 | 34 | neo4j display 35 | 36 | 37 | 38 | 39 | 45 | 46 | 47 | 48 |
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-------------------------------------------------------------------------------- 1 | from .exceptions import * 2 | 3 | class NodeView: 4 | def __init__(self, graph): 5 | self.graph = graph 6 | 7 | def __iter__(self): 8 | return iter(self.__call__()) 9 | 10 | number_of_nodes_query = """\ 11 | MATCH (:`%s`) 12 | RETURN count(*) AS numberOfNodes 13 | """ 14 | 15 | def __len__(self): 16 | with self.graph.driver.session() as session: 17 | query = self.number_of_nodes_query % self.graph.node_label 18 | return session.run(query).peek()["numberOfNodes"] 19 | 20 | get_node_attributes_query = """\ 21 | MATCH (node:`%s` {`%s`: $value }) 22 | RETURN node 23 | """ 24 | 25 | def __getitem__(self, index): 26 | with self.graph.driver.session() as session: 27 | query = self.get_node_attributes_query % ( 28 | self.graph.node_label, 29 | self.graph.identifier_property 30 | ) 31 | key = self.graph.identifier_property 32 | n = session.run(query, {"value": index}).single()["node"] 33 | data = {k: n[k] for k in n.keys() if k!=key} 34 | return data 35 | 36 | get_nodes_query = """\ 37 | MATCH (node:`%s`) 38 | RETURN node 39 | """ 40 | 41 | def __call__(self, data=False, default=None): 42 | with self.graph.driver.session() as session: 43 | query = self.get_nodes_query % (self.graph.node_label) 44 | nodes = [r["node"] for r in session.run(query).data()] 45 | key = self.graph.identifier_property 46 | if not data: 47 | for n in nodes: 48 | yield n[key] 49 | elif isinstance(data, bool): 50 | for n in nodes: 51 | rdata = {k: n[k] for k in n.keys() if k!=key} 52 | yield (n[key], rdata) 53 | else: 54 | for n in nodes: 55 | yield n[key], n.get(data, default) 56 | 57 | class EdgeView: 58 | def __init__(self, graph): 59 | self.graph = graph 60 | 61 | def __iter__(self): 62 | return iter(self.__call__()) 63 | 64 | number_of_edges_query = """\ 65 | MATCH (u:`%s`)-[edge:`%s`]->(v:`%s`) 66 | RETURN COUNT(edge) AS numberOfEdges 67 | """ 68 | 69 | def __len__(self): 70 | with self.graph.driver.session() as session: 71 | query = self.number_of_edges_query % ( 72 | self.graph.node_label, 73 | self.graph.relationship_type, 74 | self.graph.node_label 75 | ) 76 | return session.run(query).peek()["numberOfEdges"] 77 | 78 | get_edges_query = """\ 79 | MATCH (u:`%s`)-[edge:`%s`]->(v:`%s`) 80 | RETURN u.`%s` AS u, v.`%s` AS v, edge 81 | """ 82 | 83 | def __call__(self, data=False, default=None): 84 | if self.graph.relationship_type is None: 85 | return # raises StopIteration 86 | 87 | with self.graph.driver.session() as session: 88 | query = self.get_edges_query % ( 89 | self.graph.node_label, 90 | self.graph.relationship_type, 91 | self.graph.node_label, 92 | self.graph.identifier_property, 93 | self.graph.identifier_property 94 | ) 95 | edges = [(r["u"], r["v"], r["edge"]._properties) for r in session.run(query)] 96 | if not data: 97 | for u, v, _ in edges: 98 | yield (u, v) 99 | elif isinstance(data, bool): 100 | for u, v, d in edges: 101 | yield (u, v, d) 102 | else: 103 | for u, v, d in edges: 104 | yield (u, v, d.get(data, default)) 105 | 106 | 107 | class BaseGraph: 108 | def __init__(self, driver, direction, config=None): 109 | if config is None: 110 | config = {} 111 | 112 | self.driver = driver 113 | self.direction = direction 114 | self.node_label = config.get("node_label", "Node") 115 | self.relationship_type = config.get("relationship_type", "CONNECTED") 116 | self.graph = config.get("graph", "heavy") 117 | self.identifier_property = config.get("identifier_property", "id") 118 | 119 | def base_params(self): 120 | return { 121 | "direction": self.direction, 122 | "node_label": self.node_label, 123 | "relationship_type": self.relationship_type, 124 | "identifier_property": self.identifier_property 125 | # "graph": self.graph 126 | } 127 | 128 | add_node_query = """\ 129 | MERGE (:`%s` {`%s`: $node }) 130 | """ 131 | 132 | def __iter__(self): 133 | return iter(self.nodes) 134 | 135 | def __contains__(self, n): 136 | return n in self.nodes 137 | 138 | def __len__(self): 139 | return len(self.nodes) 140 | 141 | def number_of_nodes(self): 142 | return len(self.nodes) 143 | 144 | @property 145 | def nodes(self): 146 | # Lazy View creation, like in networkx 147 | nodes = NodeView(self) 148 | self.__dict__["nodes"] = nodes 149 | return nodes 150 | 151 | @property 152 | def edges(self): 153 | edges = EdgeView(self) 154 | self.__dict__["edges"] = edges 155 | return edges 156 | 157 | 158 | add_node_query = """\ 159 | MERGE (:`%s` {`%s`: $value }) 160 | """ 161 | 162 | add_node_query_with_props = """\ 163 | MERGE (n:`%s` {`%s`: $value }) 164 | ON CREATE SET n+=$props 165 | """ 166 | def add_node(self, value, attr_dict=dict(), **attr): 167 | with self.driver.session() as session: 168 | if len(attr_dict) == 0 and len(attr) == 0: 169 | query = self.add_node_query % (self.node_label, self.identifier_property) 170 | session.run(query, {"value": value}) 171 | else: 172 | props = dict(attr_dict) 173 | for k, v in attr.items(): 174 | props[k] = v 175 | query = self.add_node_query_with_props % (self.node_label, self.identifier_property) 176 | session.run(query, {"value": value}, props=props) 177 | 178 | add_nodes_query = """\ 179 | UNWIND $values AS value 180 | MERGE (:`%s` {`%s`: value }) 181 | """ 182 | 183 | add_nodes_query_with_attrdict = """\ 184 | UNWIND $values AS props 185 | MERGE (n:`%s` {`%s`: props.`%s` }) 186 | ON CREATE SET n=props 187 | """ 188 | 189 | def add_nodes_from(self, values, **attr): 190 | are_node_attrdict_tuple = False 191 | try: 192 | for v in values: 193 | if isinstance(v[1], dict): 194 | are_node_attrdict_tuple = True 195 | break 196 | except: 197 | pass 198 | 199 | if are_node_attrdict_tuple or len(attr) > 0: 200 | query = self.add_nodes_query_with_attrdict % ( 201 | self.node_label, 202 | self.identifier_property, 203 | self.identifier_property 204 | ) 205 | n_values = [] 206 | for i in values: 207 | n_d = dict(attr) 208 | if are_node_attrdict_tuple: 209 | n_d.update(i[1]) 210 | if self.identifier_property not in i[1]: 211 | n_d[self.identifier_property] = i[0] 212 | else: 213 | n_d[self.identifier_property] = i 214 | n_values.append(n_d) 215 | values = n_values 216 | else: 217 | query = self.add_nodes_query % (self.node_label, self.identifier_property) 218 | 219 | with self.driver.session() as session: 220 | session.run(query, {"values": values}) 221 | 222 | add_edge_query = """\ 223 | MERGE (node1:`%s` {`%s`: $node1 }) 224 | MERGE (node2:`%s` {`%s`: $node2 }) 225 | MERGE (node1)-[r:`%s`]->(node2) 226 | ON CREATE SET r=$props 227 | """ 228 | 229 | def add_edge(self, node1, node2, **attr): 230 | with self.driver.session() as session: 231 | query = self.add_edge_query % ( 232 | self.node_label, 233 | self.identifier_property, 234 | self.node_label, 235 | self.identifier_property, 236 | self.relationship_type 237 | ) 238 | session.run(query, {"node1": node1, "node2": node2}, props=attr) 239 | 240 | add_edges_query = """\ 241 | UNWIND $edges AS edge 242 | MERGE (node1:`%s` {`%s`: edge[0] }) 243 | MERGE (node2:`%s` {`%s`: edge[1] }) 244 | MERGE (node1)-[r:`%s`]->(node2) 245 | ON CREATE SET r=edge[2] 246 | """ 247 | 248 | def add_edges_from(self, edges, **attr): 249 | with self.driver.session() as session: 250 | query = self.add_edges_query % ( 251 | self.node_label, 252 | self.identifier_property, 253 | self.node_label, 254 | self.identifier_property, 255 | self.relationship_type 256 | ) 257 | def fix_edge(edge): 258 | if len(edge) == 2: 259 | edge.append({}) 260 | return edge 261 | session.run(query, {"edges": [fix_edge(list(edge)) for edge in edges]}) 262 | 263 | def add_path(self, path, **attr): 264 | for u, v in itertools.izip(path, path[1:]): 265 | self.add_edge(u, v, **attr) 266 | 267 | remove_node_query = """\ 268 | MATCH (n:`%s` {`%s`: $value }) 269 | DETACH DELETE n 270 | RETURN COUNT(*) AS deletedNodes; 271 | """ 272 | 273 | def remove_node(self, n): 274 | with self.driver.session() as session: 275 | query = self.remove_node_query % (self.node_label, self.identifier_property) 276 | deleted_nodes = session.run(query, {"value": n}).peek()["deletedNodes"] 277 | if deleted_nodes < 1: 278 | raise NetworkXError("The node %s is not in the graph." % (n, )) 279 | 280 | remove_nodes_query = """\ 281 | UNWIND $nodes as value 282 | MERGE (n:`%s` {`%s`: value }) 283 | DETACH DELETE n 284 | """ 285 | 286 | def remove_nodes_from(self, nodes): 287 | with self.driver.session() as session: 288 | query = self.remove_nodes_query % (self.node_label, self.identifier_property) 289 | session.run(query, {"nodes": nodes}) 290 | 291 | def update(self, edges=None, nodes=None, graph_id_props=None): 292 | if edges is not None: 293 | if nodes is not None: 294 | self.add_nodes_from(edges) 295 | self.add_edges_from(nodes) 296 | else: 297 | try: 298 | graph_nodes = edges.nodes 299 | graph_edges = edges.edges 300 | except: 301 | self.add_edges_from(edges) 302 | else: 303 | graph_nodes_data = graph_nodes(data=True) 304 | graph_edges_data = graph_edges(data=True) 305 | adding_edges = [] 306 | for u, v, data in graph_edges_data: 307 | try: 308 | if self.identifier_property in graph_nodes[u]: 309 | u = graph_nodes[u][self.identifier_property] 310 | except: 311 | pass 312 | try: 313 | if self.identifier_property in graph_nodes[v]: 314 | v = graph_nodes[v][self.identifier_property] 315 | except: 316 | pass 317 | adding_edges.append((u, v, data)) 318 | graph_nodes_data = graph_nodes(data=True) 319 | graph_nodes_fixed_data = [] 320 | for n, d in graph_nodes_data: 321 | if graph_id_props is not None: 322 | if isinstance(graph_id_props, tuple) or isinstance(graph_id_props, list): 323 | for value, v in zip(n, graph_id_props): 324 | d[v] = value 325 | else: 326 | d[graph_id_props] = n 327 | graph_nodes_fixed_data.append((n, d)) 328 | self.add_nodes_from(graph_nodes_fixed_data) 329 | self.add_edges_from(adding_edges) 330 | 331 | _clear_graph_nodes_query = """\ 332 | MATCH (n:`%s`) 333 | DELETE n 334 | """ 335 | 336 | _clear_graph_edges_query = """\ 337 | MATCH (:`%s`)-[r:`%s`]-(:`%s`) 338 | DELETE r 339 | """ 340 | 341 | def clear(self): 342 | with self.driver.session() as session: 343 | if self.relationship_type: 344 | query = self._clear_graph_edges_query % ( 345 | self.node_label, 346 | self.relationship_type, 347 | self.node_label 348 | ) 349 | session.run(query) 350 | query = self._clear_graph_nodes_query % (self.node_label) 351 | session.run(query) 352 | def number_of_nodes(self): 353 | with self.driver.session() as session: 354 | query = self.number_of_nodes_query % self.node_label 355 | return session.run(query).peek()["numberOfNodes"] 356 | 357 | 358 | def delete_all(self): 359 | with self.driver.session() as session: 360 | session.run("MATCH (n) DETACH DELETE n") 361 | 362 | def load_got(self): 363 | """ 364 | Author: Andrew Beveridge 365 | https://twitter.com/mathbeveridge 366 | """ 367 | with self.driver.session() as session: 368 | session.run("""\ 369 | CREATE CONSTRAINT ON (c:Character) ASSERT c.name IS UNIQUE; 370 | """) 371 | 372 | session.run("""\ 373 | LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/mathbeveridge/asoiaf/master/data/asoiaf-book1-edges.csv" AS row 374 | MERGE (src:Character {name: row.Source}) 375 | MERGE (tgt:Character {name: row.Target}) 376 | // relationship for the book 377 | MERGE (src)-[r:INTERACTS1]->(tgt) 378 | ON CREATE SET r.weight = toInteger(row.weight), r.book=1 379 | """) 380 | 381 | session.run("""\ 382 | LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/mathbeveridge/asoiaf/master/data/asoiaf-book2-edges.csv" AS row 383 | MERGE (src:Character {name: row.Source}) 384 | MERGE (tgt:Character {name: row.Target}) 385 | // relationship for the book 386 | MERGE (src)-[r:INTERACTS2]->(tgt) 387 | ON CREATE SET r.weight = toInteger(row.weight), r.book=2 388 | """) 389 | 390 | session.run("""\ 391 | LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/mathbeveridge/asoiaf/master/data/asoiaf-book3-edges.csv" AS row 392 | MERGE (src:Character {name: row.Source}) 393 | MERGE (tgt:Character {name: row.Target}) 394 | // relationship for the book 395 | MERGE (src)-[r:INTERACTS3]->(tgt) 396 | ON CREATE SET r.weight = toInteger(row.weight), r.book=3 397 | """) 398 | 399 | session.run("""\ 400 | LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/mathbeveridge/asoiaf/master/data/asoiaf-book45-edges.csv" AS row 401 | MERGE (src:Character {name: row.Source}) 402 | MERGE (tgt:Character {name: row.Target}) 403 | // relationship for the book 404 | MERGE (src)-[r:INTERACTS45]->(tgt) 405 | ON CREATE SET r.weight = toInteger(row.weight), r.book=45 406 | """) 407 | with self.driver.session() as session: 408 | session.run("""\ 409 | DROP CONSTRAINT ON (c:Character) ASSERT c.name IS UNIQUE; 410 | """) 411 | 412 | def load_euroads(self): 413 | with self.driver.session() as session: 414 | session.run("""\ 415 | CREATE CONSTRAINT ON (p:Place) ASSERT p.name IS UNIQUE 416 | """) 417 | session.run("""\ 418 | USING PERIODIC COMMIT 1000 419 | LOAD CSV WITH HEADERS FROM "https://github.com/neo4j-apps/neuler/raw/master/sample-data/eroads/roads.csv" 420 | AS row 421 | 422 | MERGE (origin:Place {name: row.origin_reference_place}) 423 | SET origin.countryCode = row.origin_country_code 424 | 425 | MERGE (destination:Place {name: row.destination_reference_place}) 426 | SET destination.countryCode = row.destination_country_code 427 | 428 | MERGE (origin)-[eroad:EROAD {road_number: row.road_number}]->(destination) 429 | SET eroad.distance = toInteger(row.distance), eroad.watercrossing = row.watercrossing 430 | """) 431 | with self.driver.session() as session: 432 | session.run("""\ 433 | DROP CONSTRAINT ON (p:Place) ASSERT p.name IS UNIQUE 434 | """) 435 | def load_twitter(self): 436 | with self.driver.session() as session: 437 | session.run("""\ 438 | CREATE CONSTRAINT ON(u:User) ASSERT u.id IS unique 439 | """) 440 | 441 | session.run("""\ 442 | CALL apoc.load.json("https://github.com/neo4j-apps/neuler/raw/master/sample-data/twitter/users.json") 443 | YIELD value 444 | MERGE (u:User {id: value.user.id }) 445 | SET u += value.user 446 | FOREACH (following IN value.following | 447 | MERGE (f1:User {id: following}) 448 | MERGE (u)-[:FOLLOWS]->(f1)) 449 | FOREACH (follower IN value.followers | 450 | MERGE(f2:User {id: follower}) 451 | MERGE (u)<-[:FOLLOWS]-(f2)); 452 | """) 453 | with self.driver.session() as session: 454 | session.run("""\ 455 | DROP CONSTRAINT ON(u:User) ASSERT u.id IS unique 456 | """) 457 | -------------------------------------------------------------------------------- /examples/nxneo4j_tutorial_latest.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Welcome to nxneo4j!\n", 8 | "#### nxneo4j is a library that enables you to use networkX type of commands to interact with Neo4j. " 9 | ] 10 | }, 11 | { 12 | "cell_type": "markdown", 13 | "metadata": {}, 14 | "source": [ 15 | "### _Latest version is 0.0.3_\n", 16 | "If not already installed, install the latest version like this:" 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": null, 22 | "metadata": {}, 23 | "outputs": [], 24 | "source": [ 25 | "! pip uninstall -y networkx-neo4j #remove the old installation" 26 | ] 27 | }, 28 | { 29 | "cell_type": "code", 30 | "execution_count": null, 31 | "metadata": {}, 32 | "outputs": [], 33 | "source": [ 34 | "! pip install git+https://github.com/ybaktir/networkx-neo4j #install the latest one" 35 | ] 36 | }, 37 | { 38 | "cell_type": "code", 39 | "execution_count": 1, 40 | "metadata": { 41 | "scrolled": true 42 | }, 43 | "outputs": [ 44 | { 45 | "name": "stdout", 46 | "output_type": "stream", 47 | "text": [ 48 | "Last run on: 2020-08-31 19:54:34 ('CST', 'CDT')\n" 49 | ] 50 | } 51 | ], 52 | "source": [ 53 | "import datetime, time\n", 54 | "print ('Last run on: ' + datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\") + ' ' + repr(time.tzname))" 55 | ] 56 | }, 57 | { 58 | "cell_type": "markdown", 59 | "metadata": {}, 60 | "source": [ 61 | "## Connect to Neo4j" 62 | ] 63 | }, 64 | { 65 | "cell_type": "code", 66 | "execution_count": 2, 67 | "metadata": {}, 68 | "outputs": [], 69 | "source": [ 70 | "from neo4j import GraphDatabase" 71 | ] 72 | }, 73 | { 74 | "cell_type": "code", 75 | "execution_count": 4, 76 | "metadata": {}, 77 | "outputs": [], 78 | "source": [ 79 | "driver = GraphDatabase.driver(uri=\"bolt://localhost:11007\",auth=(\"neo4j\",\"your_password\"))\n", 80 | " #OR \"bolt://localhost:7673\"\n", 81 | " #OR the cloud url" 82 | ] 83 | }, 84 | { 85 | "cell_type": "code", 86 | "execution_count": 5, 87 | "metadata": {}, 88 | "outputs": [], 89 | "source": [ 90 | "import nxneo4j as nx" 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": 6, 96 | "metadata": {}, 97 | "outputs": [], 98 | "source": [ 99 | "G = nx.Graph(driver)" 100 | ] 101 | }, 102 | { 103 | "cell_type": "code", 104 | "execution_count": 7, 105 | "metadata": {}, 106 | "outputs": [], 107 | "source": [ 108 | "G.delete_all() #This will delete all the data, be careful\n", 109 | " #Just making sure that the results are reprodusible." 110 | ] 111 | }, 112 | { 113 | "cell_type": "markdown", 114 | "metadata": {}, 115 | "source": [ 116 | "## Add Nodes" 117 | ] 118 | }, 119 | { 120 | "cell_type": "code", 121 | "execution_count": 8, 122 | "metadata": {}, 123 | "outputs": [], 124 | "source": [ 125 | "#Add a node\n", 126 | "G.add_node(\"Yusuf\")" 127 | ] 128 | }, 129 | { 130 | "cell_type": "code", 131 | "execution_count": 9, 132 | "metadata": {}, 133 | "outputs": [], 134 | "source": [ 135 | "#Add node with features\n", 136 | "G.add_node(\"Nurgul\",gender='F')" 137 | ] 138 | }, 139 | { 140 | "cell_type": "code", 141 | "execution_count": 10, 142 | "metadata": {}, 143 | "outputs": [], 144 | "source": [ 145 | "#Add multiple properties at once\n", 146 | "G.add_node(\"Betul\",age=4,gender='F')" 147 | ] 148 | }, 149 | { 150 | "cell_type": "code", 151 | "execution_count": 11, 152 | "metadata": {}, 153 | "outputs": [ 154 | { 155 | "name": "stdout", 156 | "output_type": "stream", 157 | "text": [ 158 | "Yusuf\n", 159 | "Nurgul\n", 160 | "Betul\n" 161 | ] 162 | } 163 | ], 164 | "source": [ 165 | "#Check nodes\n", 166 | "for node in G.nodes(): #Unlike networkX, nxneo4j returns a generator\n", 167 | " print(node)" 168 | ] 169 | }, 170 | { 171 | "cell_type": "code", 172 | "execution_count": 12, 173 | "metadata": {}, 174 | "outputs": [ 175 | { 176 | "data": { 177 | "text/plain": [ 178 | "['Yusuf', 'Nurgul', 'Betul']" 179 | ] 180 | }, 181 | "execution_count": 12, 182 | "metadata": {}, 183 | "output_type": "execute_result" 184 | } 185 | ], 186 | "source": [ 187 | "#Or simply\n", 188 | "list(G.nodes())" 189 | ] 190 | }, 191 | { 192 | "cell_type": "code", 193 | "execution_count": 13, 194 | "metadata": {}, 195 | "outputs": [ 196 | { 197 | "data": { 198 | "text/plain": [ 199 | "[('Yusuf', {}),\n", 200 | " ('Nurgul', {'gender': 'F'}),\n", 201 | " ('Betul', {'gender': 'F', 'age': 4})]" 202 | ] 203 | }, 204 | "execution_count": 13, 205 | "metadata": {}, 206 | "output_type": "execute_result" 207 | } 208 | ], 209 | "source": [ 210 | "#Get the data associated with each node\n", 211 | "list(G.nodes(data=True))" 212 | ] 213 | }, 214 | { 215 | "cell_type": "code", 216 | "execution_count": 14, 217 | "metadata": {}, 218 | "outputs": [ 219 | { 220 | "data": { 221 | "text/plain": [ 222 | "3" 223 | ] 224 | }, 225 | "execution_count": 14, 226 | "metadata": {}, 227 | "output_type": "execute_result" 228 | } 229 | ], 230 | "source": [ 231 | "#number of nodes\n", 232 | "len(G)" 233 | ] 234 | }, 235 | { 236 | "cell_type": "code", 237 | "execution_count": 15, 238 | "metadata": {}, 239 | "outputs": [ 240 | { 241 | "data": { 242 | "text/html": [ 243 | "\n", 244 | " \n", 251 | " " 252 | ], 253 | "text/plain": [ 254 | "" 255 | ] 256 | }, 257 | "execution_count": 15, 258 | "metadata": {}, 259 | "output_type": "execute_result" 260 | } 261 | ], 262 | "source": [ 263 | "#Display\n", 264 | "nx.draw(G) #It is interactive, drag the nodes!" 265 | ] 266 | }, 267 | { 268 | "cell_type": "code", 269 | "execution_count": 16, 270 | "metadata": {}, 271 | "outputs": [ 272 | { 273 | "data": { 274 | "text/plain": [ 275 | "{'gender': 'F', 'age': 4}" 276 | ] 277 | }, 278 | "execution_count": 16, 279 | "metadata": {}, 280 | "output_type": "execute_result" 281 | } 282 | ], 283 | "source": [ 284 | "#Check a particular node feature\n", 285 | "G.nodes['Betul']" 286 | ] 287 | }, 288 | { 289 | "cell_type": "code", 290 | "execution_count": 17, 291 | "metadata": {}, 292 | "outputs": [ 293 | { 294 | "data": { 295 | "text/plain": [ 296 | "4" 297 | ] 298 | }, 299 | "execution_count": 17, 300 | "metadata": {}, 301 | "output_type": "execute_result" 302 | } 303 | ], 304 | "source": [ 305 | "#You can be more specific\n", 306 | "G.nodes['Betul']['age']" 307 | ] 308 | }, 309 | { 310 | "cell_type": "code", 311 | "execution_count": 18, 312 | "metadata": {}, 313 | "outputs": [], 314 | "source": [ 315 | "G.add_nodes_from([1,2,3,4])" 316 | ] 317 | }, 318 | { 319 | "cell_type": "code", 320 | "execution_count": 19, 321 | "metadata": {}, 322 | "outputs": [ 323 | { 324 | "data": { 325 | "text/plain": [ 326 | "['Yusuf', 'Nurgul', 'Betul', 1, 2, 3, 4]" 327 | ] 328 | }, 329 | "execution_count": 19, 330 | "metadata": {}, 331 | "output_type": "execute_result" 332 | } 333 | ], 334 | "source": [ 335 | "list(G.nodes())" 336 | ] 337 | }, 338 | { 339 | "cell_type": "markdown", 340 | "metadata": {}, 341 | "source": [ 342 | "## Add Edges" 343 | ] 344 | }, 345 | { 346 | "cell_type": "code", 347 | "execution_count": 20, 348 | "metadata": {}, 349 | "outputs": [], 350 | "source": [ 351 | "#Add one edge\n", 352 | "G.add_edge('Yusuf','Max')" 353 | ] 354 | }, 355 | { 356 | "cell_type": "code", 357 | "execution_count": 21, 358 | "metadata": {}, 359 | "outputs": [ 360 | { 361 | "data": { 362 | "text/html": [ 363 | "\n", 364 | " \n", 371 | " " 372 | ], 373 | "text/plain": [ 374 | "" 375 | ] 376 | }, 377 | "execution_count": 21, 378 | "metadata": {}, 379 | "output_type": "execute_result" 380 | } 381 | ], 382 | "source": [ 383 | "nx.draw(G) #default relationship label is \"CONNECTED\"" 384 | ] 385 | }, 386 | { 387 | "cell_type": "code", 388 | "execution_count": 22, 389 | "metadata": {}, 390 | "outputs": [], 391 | "source": [ 392 | "#You can change the default connection label like the following\n", 393 | "G.relationship_type = 'LOVES'" 394 | ] 395 | }, 396 | { 397 | "cell_type": "code", 398 | "execution_count": 23, 399 | "metadata": {}, 400 | "outputs": [], 401 | "source": [ 402 | "G.add_edge('Yusuf','Nurgul')\n", 403 | "G.add_edge('Nurgul','Yusuf')" 404 | ] 405 | }, 406 | { 407 | "cell_type": "code", 408 | "execution_count": 24, 409 | "metadata": {}, 410 | "outputs": [ 411 | { 412 | "data": { 413 | "text/html": [ 414 | "\n", 415 | " \n", 422 | " " 423 | ], 424 | "text/plain": [ 425 | "" 426 | ] 427 | }, 428 | "execution_count": 24, 429 | "metadata": {}, 430 | "output_type": "execute_result" 431 | } 432 | ], 433 | "source": [ 434 | "nx.draw(G)" 435 | ] 436 | }, 437 | { 438 | "cell_type": "code", 439 | "execution_count": 25, 440 | "metadata": {}, 441 | "outputs": [], 442 | "source": [ 443 | "#You can add properties as well\n", 444 | "G.add_edge('Betul','Nurgul',how_much='More than Dad')" 445 | ] 446 | }, 447 | { 448 | "cell_type": "code", 449 | "execution_count": 26, 450 | "metadata": {}, 451 | "outputs": [ 452 | { 453 | "data": { 454 | "text/plain": [ 455 | "[('Yusuf', 'Nurgul', {}),\n", 456 | " ('Nurgul', 'Yusuf', {}),\n", 457 | " ('Betul', 'Nurgul', {'how_much': 'More than Dad'})]" 458 | ] 459 | }, 460 | "execution_count": 26, 461 | "metadata": {}, 462 | "output_type": "execute_result" 463 | } 464 | ], 465 | "source": [ 466 | "#display the values\n", 467 | "list(G.edges(data=True))" 468 | ] 469 | }, 470 | { 471 | "cell_type": "code", 472 | "execution_count": 27, 473 | "metadata": {}, 474 | "outputs": [], 475 | "source": [ 476 | "G.relationship_type = 'CONNECTED'" 477 | ] 478 | }, 479 | { 480 | "cell_type": "code", 481 | "execution_count": 28, 482 | "metadata": {}, 483 | "outputs": [], 484 | "source": [ 485 | "G.add_edges_from([(1,2),(3,4)])" 486 | ] 487 | }, 488 | { 489 | "cell_type": "code", 490 | "execution_count": 29, 491 | "metadata": {}, 492 | "outputs": [ 493 | { 494 | "data": { 495 | "text/html": [ 496 | "\n", 497 | " \n", 504 | " " 505 | ], 506 | "text/plain": [ 507 | "" 508 | ] 509 | }, 510 | "execution_count": 29, 511 | "metadata": {}, 512 | "output_type": "execute_result" 513 | } 514 | ], 515 | "source": [ 516 | "nx.draw(G)" 517 | ] 518 | }, 519 | { 520 | "cell_type": "markdown", 521 | "metadata": {}, 522 | "source": [ 523 | "## Remove Nodes" 524 | ] 525 | }, 526 | { 527 | "cell_type": "code", 528 | "execution_count": 30, 529 | "metadata": {}, 530 | "outputs": [], 531 | "source": [ 532 | "G.remove_node('Yusuf')" 533 | ] 534 | }, 535 | { 536 | "cell_type": "code", 537 | "execution_count": 31, 538 | "metadata": {}, 539 | "outputs": [ 540 | { 541 | "data": { 542 | "text/plain": [ 543 | "['Nurgul', 'Betul', 1, 2, 3, 4, 'Max']" 544 | ] 545 | }, 546 | "execution_count": 31, 547 | "metadata": {}, 548 | "output_type": "execute_result" 549 | } 550 | ], 551 | "source": [ 552 | "list(G.nodes())" 553 | ] 554 | }, 555 | { 556 | "cell_type": "markdown", 557 | "metadata": {}, 558 | "source": [ 559 | "## Graph Data Science" 560 | ] 561 | }, 562 | { 563 | "cell_type": "markdown", 564 | "metadata": {}, 565 | "source": [ 566 | "There are several builtin graph algorithms in Neo4j. nxneo4j will expand to cover all of them in the future versions. For now, the following networkX algorithms are supported: \n", 567 | "- pagerank\n", 568 | "- betweenness_centrality\n", 569 | "- closeness_centrality\n", 570 | "- label_propagation\n", 571 | "- connected_components\n", 572 | "- clustering \n", 573 | "- triangles\n", 574 | "- shortest_path\n", 575 | "- shortest_weighted_path\n", 576 | "\n", 577 | "Let's delete all data and load GOT data:" 578 | ] 579 | }, 580 | { 581 | "cell_type": "code", 582 | "execution_count": 32, 583 | "metadata": {}, 584 | "outputs": [], 585 | "source": [ 586 | "G.delete_all()\n", 587 | "G.load_got()" 588 | ] 589 | }, 590 | { 591 | "cell_type": "code", 592 | "execution_count": 33, 593 | "metadata": {}, 594 | "outputs": [], 595 | "source": [ 596 | "#You can change the default parameters like the following:\n", 597 | "G.identifier_property = 'name'\n", 598 | "G.relationship_type = '*'\n", 599 | "G.node_label = 'Character'" 600 | ] 601 | }, 602 | { 603 | "cell_type": "code", 604 | "execution_count": 34, 605 | "metadata": {}, 606 | "outputs": [ 607 | { 608 | "data": { 609 | "text/html": [ 610 | "\n", 611 | " \n", 618 | " " 619 | ], 620 | "text/plain": [ 621 | "" 622 | ] 623 | }, 624 | "execution_count": 34, 625 | "metadata": {}, 626 | "output_type": "execute_result" 627 | } 628 | ], 629 | "source": [ 630 | "nx.draw(G) #Zoom in to see the names :)" 631 | ] 632 | }, 633 | { 634 | "cell_type": "code", 635 | "execution_count": 34, 636 | "metadata": {}, 637 | "outputs": [ 638 | { 639 | "data": { 640 | "text/plain": [ 641 | "796" 642 | ] 643 | }, 644 | "execution_count": 34, 645 | "metadata": {}, 646 | "output_type": "execute_result" 647 | } 648 | ], 649 | "source": [ 650 | "len(G) #796 nodes" 651 | ] 652 | }, 653 | { 654 | "cell_type": "markdown", 655 | "metadata": {}, 656 | "source": [ 657 | "## 1. Centrality Algorithms" 658 | ] 659 | }, 660 | { 661 | "cell_type": "markdown", 662 | "metadata": {}, 663 | "source": [ 664 | "We’ll start with the famous PageRank algorithm. Let’s find out who the most influential characters in Game of Thrones are:" 665 | ] 666 | }, 667 | { 668 | "cell_type": "markdown", 669 | "metadata": {}, 670 | "source": [ 671 | "### Pagerank" 672 | ] 673 | }, 674 | { 675 | "cell_type": "markdown", 676 | "metadata": {}, 677 | "source": [ 678 | "We’ll start with the famous PageRank algorithm. Let’s find out who the most influential characters in Game of Thrones are:" 679 | ] 680 | }, 681 | { 682 | "cell_type": "code", 683 | "execution_count": null, 684 | "metadata": {}, 685 | "outputs": [], 686 | "source": [ 687 | "nx.pagerank(G) #RAW OUTPUT" 688 | ] 689 | }, 690 | { 691 | "cell_type": "code", 692 | "execution_count": 36, 693 | "metadata": {}, 694 | "outputs": [ 695 | { 696 | "name": "stdout", 697 | "output_type": "stream", 698 | "text": [ 699 | "Jon-Snow 17.596909502156667\n", 700 | "Tyrion-Lannister 17.568136240123653\n", 701 | "Jaime-Lannister 13.925499376200438\n", 702 | "Cersei-Lannister 13.402380343770089\n", 703 | "Daenerys-Targaryen 12.499217151004583\n", 704 | "Stannis-Baratheon 12.15039813708843\n", 705 | "Arya-Stark 11.69211189582387\n", 706 | "Robb-Stark 11.277725861477968\n", 707 | "Eddard-Stark 10.68388151188578\n", 708 | "Catelyn-Stark 10.619218634539562\n" 709 | ] 710 | } 711 | ], 712 | "source": [ 713 | "# the most influential characters\n", 714 | "response = nx.pagerank(G)\n", 715 | "sorted_pagerank = sorted(response.items(), key=lambda x: x[1], reverse=True)\n", 716 | "for character, score in sorted_pagerank[:10]:\n", 717 | " print(character, score)" 718 | ] 719 | }, 720 | { 721 | "cell_type": "markdown", 722 | "metadata": {}, 723 | "source": [ 724 | "### Betweenness centrality" 725 | ] 726 | }, 727 | { 728 | "cell_type": "markdown", 729 | "metadata": {}, 730 | "source": [ 731 | "We can also run betweenness centrality over the dataset. This algorithm will tell us which nodes are the most 'pivotal' i.e. how many of the shortest paths between pairs of characters must pass through them" 732 | ] 733 | }, 734 | { 735 | "cell_type": "code", 736 | "execution_count": null, 737 | "metadata": {}, 738 | "outputs": [], 739 | "source": [ 740 | "# Betweenness centrality\n", 741 | "nx.betweenness_centrality(G) #RAW OUTPUT" 742 | ] 743 | }, 744 | { 745 | "cell_type": "code", 746 | "execution_count": 38, 747 | "metadata": {}, 748 | "outputs": [ 749 | { 750 | "name": "stdout", 751 | "output_type": "stream", 752 | "text": [ 753 | "Jon-Snow 65395.26787165435\n", 754 | "Tyrion-Lannister 50202.17398521847\n", 755 | "Daenerys-Targaryen 39636.77718662114\n", 756 | "Stannis-Baratheon 35984.21182863314\n", 757 | "Theon-Greyjoy 35436.85268519103\n", 758 | "Jaime-Lannister 32122.976615424588\n", 759 | "Robert-Baratheon 31391.065251945023\n", 760 | "Arya-Stark 29342.15853062157\n", 761 | "Cersei-Lannister 28274.915426635584\n", 762 | "Eddard-Stark 26470.250249098248\n" 763 | ] 764 | } 765 | ], 766 | "source": [ 767 | "# RANKED OUTPUT\n", 768 | "response = nx.betweenness_centrality(G)\n", 769 | "\n", 770 | "sorted_bw = sorted(response.items(), key=lambda x: x[1], reverse=True)\n", 771 | "for character, score in sorted_bw[:10]:\n", 772 | " print(character, score)" 773 | ] 774 | }, 775 | { 776 | "cell_type": "markdown", 777 | "metadata": {}, 778 | "source": [ 779 | "### Closeness centrality\n", 780 | "\n", 781 | "Closeness centrality tells us on average how many hops away each character is from every other character." 782 | ] 783 | }, 784 | { 785 | "cell_type": "code", 786 | "execution_count": null, 787 | "metadata": {}, 788 | "outputs": [], 789 | "source": [ 790 | "# Closeness centrality\n", 791 | "nx.closeness_centrality(G) #RAW OUTPUT" 792 | ] 793 | }, 794 | { 795 | "cell_type": "code", 796 | "execution_count": 40, 797 | "metadata": {}, 798 | "outputs": [ 799 | { 800 | "name": "stdout", 801 | "output_type": "stream", 802 | "text": [ 803 | "Tyrion-Lannister 0.4763331336129419\n", 804 | "Robert-Baratheon 0.4592720970537262\n", 805 | "Eddard-Stark 0.455848623853211\n", 806 | "Cersei-Lannister 0.45454545454545453\n", 807 | "Jaime-Lannister 0.4519613416714042\n", 808 | "Jon-Snow 0.44537815126050423\n", 809 | "Stannis-Baratheon 0.4446308724832215\n", 810 | "Robb-Stark 0.4441340782122905\n", 811 | "Joffrey-Baratheon 0.4339519650655022\n", 812 | "Catelyn-Stark 0.4334787350054526\n" 813 | ] 814 | } 815 | ], 816 | "source": [ 817 | "# RANKED\n", 818 | "response = nx.closeness_centrality(G)\n", 819 | "\n", 820 | "sorted_cc = sorted(response.items(), key=lambda x: x[1], reverse=True)\n", 821 | "for character, score in sorted_cc[:10]:\n", 822 | " print(character, score)" 823 | ] 824 | }, 825 | { 826 | "cell_type": "markdown", 827 | "metadata": {}, 828 | "source": [ 829 | "## 2. Community Detection Algoritms" 830 | ] 831 | }, 832 | { 833 | "cell_type": "markdown", 834 | "metadata": {}, 835 | "source": [ 836 | "### Label Propagation\n", 837 | "We can also partition the characters into communities using the label propagation algorithm" 838 | ] 839 | }, 840 | { 841 | "cell_type": "code", 842 | "execution_count": 41, 843 | "metadata": {}, 844 | "outputs": [ 845 | { 846 | "data": { 847 | "text/plain": [ 848 | "" 849 | ] 850 | }, 851 | "execution_count": 41, 852 | "metadata": {}, 853 | "output_type": "execute_result" 854 | } 855 | ], 856 | "source": [ 857 | "# Label propagation\n", 858 | "nx.label_propagation_communities(G) #RAW OUPUT is a generator" 859 | ] 860 | }, 861 | { 862 | "cell_type": "code", 863 | "execution_count": 42, 864 | "metadata": {}, 865 | "outputs": [ 866 | { 867 | "name": "stdout", 868 | "output_type": "stream", 869 | "text": [ 870 | "['Leo-Lefford', 'Ravella-Swann', 'Raynald-Westerling', 'Harwood-Stout', 'Guncer-Sunglass', 'Gawen-Westerling', 'Shagwell', 'Maron-Greyjoy', 'Sarella-Sand', 'Harl']\n", 871 | "['Xhondo', 'Orell', 'Wynton-Stout', 'Dalla', 'Tormund', 'Quhuru-Mo', 'Owen', 'Val', 'Pate-(novice)', 'Othor']\n", 872 | "['Qezza', 'Draqaz', 'Reznak-mo-Reznak', 'Hugh-Hungerford', 'Rakharo', 'Fogo', 'Ogo', 'Meris', 'Kraznys-mo-Nakloz', 'Kedry']\n" 873 | ] 874 | } 875 | ], 876 | "source": [ 877 | "communities = nx.label_propagation_communities(G)\n", 878 | "sorted_communities = sorted(communities, key=lambda x: len(x), reverse=True)\n", 879 | "for community in sorted_communities[:10]:\n", 880 | " print(list(community)[:10])" 881 | ] 882 | }, 883 | { 884 | "cell_type": "markdown", 885 | "metadata": {}, 886 | "source": [ 887 | "Characters are in the same community as those other characters with whom they frequently interact. The idea is that characters have closer ties to those in their community than to those outside.\n", 888 | "\n" 889 | ] 890 | }, 891 | { 892 | "cell_type": "markdown", 893 | "metadata": {}, 894 | "source": [ 895 | "### Clustering\n", 896 | "We can calculate the clustering coefficient for each character. A clustering coefficient of '1' means that all characters that interact with that character also interact with each other:" 897 | ] 898 | }, 899 | { 900 | "cell_type": "code", 901 | "execution_count": null, 902 | "metadata": {}, 903 | "outputs": [], 904 | "source": [ 905 | "# Clustering\n", 906 | "nx.clustering(G) #RAW OUTPUT" 907 | ] 908 | }, 909 | { 910 | "cell_type": "code", 911 | "execution_count": 44, 912 | "metadata": {}, 913 | "outputs": [ 914 | { 915 | "name": "stdout", 916 | "output_type": "stream", 917 | "text": [ 918 | "['Steffon-Baratheon', 4.0]\n", 919 | "['Oswell-Kettleblack', 4.0]\n", 920 | "['Wylis-Manderly', 4.0]\n", 921 | "['Beth-Cassel', 3.0]\n", 922 | "['Big-Boil', 3.0]\n", 923 | "['Dirk', 3.0]\n", 924 | "['Jon-Umber-(Smalljon)', 3.0]\n", 925 | "['Orell', 3.0]\n", 926 | "['Oznak-zo-Pahl', 3.0]\n", 927 | "['Mag-Mar-Tun-Doh-Weg', 3.0]\n" 928 | ] 929 | } 930 | ], 931 | "source": [ 932 | "response = nx.clustering(G)\n", 933 | "\n", 934 | "biggest_coefficient = sorted(response.items(), key=lambda x: x[1], reverse=True)\n", 935 | "for character in biggest_coefficient[:10]:\n", 936 | " print(list(character)[:10])" 937 | ] 938 | }, 939 | { 940 | "cell_type": "code", 941 | "execution_count": null, 942 | "metadata": {}, 943 | "outputs": [], 944 | "source": [ 945 | "list(nx.connected_components(G))" 946 | ] 947 | }, 948 | { 949 | "cell_type": "code", 950 | "execution_count": 46, 951 | "metadata": {}, 952 | "outputs": [ 953 | { 954 | "data": { 955 | "text/plain": [ 956 | "1" 957 | ] 958 | }, 959 | "execution_count": 46, 960 | "metadata": {}, 961 | "output_type": "execute_result" 962 | } 963 | ], 964 | "source": [ 965 | "nx.number_connected_components(G)" 966 | ] 967 | }, 968 | { 969 | "cell_type": "code", 970 | "execution_count": null, 971 | "metadata": {}, 972 | "outputs": [], 973 | "source": [ 974 | "nx.triangles(G) #RAW OUTPUT" 975 | ] 976 | }, 977 | { 978 | "cell_type": "markdown", 979 | "metadata": {}, 980 | "source": [ 981 | "## 3. Path Finding Algorithms" 982 | ] 983 | }, 984 | { 985 | "cell_type": "markdown", 986 | "metadata": {}, 987 | "source": [ 988 | "Let's find the distance between two characters" 989 | ] 990 | }, 991 | { 992 | "cell_type": "code", 993 | "execution_count": 48, 994 | "metadata": {}, 995 | "outputs": [ 996 | { 997 | "data": { 998 | "text/plain": [ 999 | "['Tyrion-Lannister', 'Luwin', 'Hodor']" 1000 | ] 1001 | }, 1002 | "execution_count": 48, 1003 | "metadata": {}, 1004 | "output_type": "execute_result" 1005 | } 1006 | ], 1007 | "source": [ 1008 | "# Shortest path\n", 1009 | "nx.shortest_path(G, source=\"Tyrion-Lannister\", target=\"Hodor\")" 1010 | ] 1011 | }, 1012 | { 1013 | "cell_type": "code", 1014 | "execution_count": 49, 1015 | "metadata": {}, 1016 | "outputs": [ 1017 | { 1018 | "data": { 1019 | "text/plain": [ 1020 | "['Tyrion-Lannister', 'Theon-Greyjoy', 'Wyman-Manderly', 'Hodor']" 1021 | ] 1022 | }, 1023 | "execution_count": 49, 1024 | "metadata": {}, 1025 | "output_type": "execute_result" 1026 | } 1027 | ], 1028 | "source": [ 1029 | "# Shortest weighted path\n", 1030 | "nx.shortest_weighted_path(G, source=\"Tyrion-Lannister\", target=\"Hodor\",weight='weight')" 1031 | ] 1032 | } 1033 | ], 1034 | "metadata": { 1035 | "kernelspec": { 1036 | "display_name": "Python 3", 1037 | "language": "python", 1038 | "name": "python3" 1039 | }, 1040 | "language_info": { 1041 | "codemirror_mode": { 1042 | "name": "ipython", 1043 | "version": 3 1044 | }, 1045 | "file_extension": ".py", 1046 | "mimetype": "text/x-python", 1047 | "name": "python", 1048 | "nbconvert_exporter": "python", 1049 | "pygments_lexer": "ipython3", 1050 | "version": "3.7.6" 1051 | } 1052 | }, 1053 | "nbformat": 4, 1054 | "nbformat_minor": 4 1055 | } 1056 | -------------------------------------------------------------------------------- /examples/vis.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | neo4j display 4 | 5 | 6 | 7 | 8 | 14 | 15 | 16 | 17 |
18 | 19 | 74 | 75 | 76 | -------------------------------------------------------------------------------- /examples/nxneo4j 0.0.2 tutorial.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## 0.0.1 vs 0.0.2" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "0.0.2 basically resurrects nxneo4j:\n", 15 | "- All \"apoc\" based code is moved to Graph Data Science library aka \"gds\" since apoc is not supported by neo4j 4x\n", 16 | "- All {params} syntax moved to $params syntax since {params} no longer supported\n", 17 | "- Added nxneo4j.Graph.delete_all() feature for quick deleting of the data\n", 18 | "- Added nxneo4j.Graph.load_got(), nxneo4j.Graph.load_twitter(), nxneo4j.Graph.load_euroads() for quick data installation\n", 19 | "\n", 20 | "Requirements:\n", 21 | "- ≥ Neo4j 4.x\n", 22 | "- Graph Data Science Library Plugin\n", 23 | "- APOC Plugin\n", 24 | "- ≥ Python 3.6\n", 25 | "- ≥ neo4j-driver 4.x\n", 26 | "\n", 27 | "KNOWN ISSUES IN 0.0.2:\n", 28 | "- nxneo4j.Graph.load_got(), nxneo4j.Graph.load_twitter(), nxneo4j.Graph.load_euroads() gives Contraint error and toInt no longer used errors. Fixed on 0.0.3\n", 29 | "- nxneo4j.betweenness_centrality(G) throws ClientError. Fixed on 0.0.3\n", 30 | "- nxneo4j.pagerank(G) throws TransientError: Database 'neo4j' unavailable\n", 31 | "- nx.triangles(G) throws ClientError. Fixed on 0.0.3\n", 32 | "- nx.clustering(G) throws ClientError. Fixed on 0.0.3" 33 | ] 34 | }, 35 | { 36 | "cell_type": "markdown", 37 | "metadata": {}, 38 | "source": [ 39 | "## Connect to Neo4j" 40 | ] 41 | }, 42 | { 43 | "cell_type": "markdown", 44 | "metadata": {}, 45 | "source": [ 46 | "STOP! Make sure you have actually started Neo4j. If the Neo4j is not already running there is nothing to connect to" 47 | ] 48 | }, 49 | { 50 | "cell_type": "code", 51 | "execution_count": 1, 52 | "metadata": {}, 53 | "outputs": [], 54 | "source": [ 55 | "from neo4j import GraphDatabase" 56 | ] 57 | }, 58 | { 59 | "cell_type": "code", 60 | "execution_count": 83, 61 | "metadata": {}, 62 | "outputs": [], 63 | "source": [ 64 | "driver = GraphDatabase.driver(uri=\"bolt://localhost:11003\",auth=(\"neo4j\",\"your_password\"))\n", 65 | " #OR \"bolt://localhost:7673\"\n", 66 | " #OR the cloud url" 67 | ] 68 | }, 69 | { 70 | "cell_type": "markdown", 71 | "metadata": {}, 72 | "source": [ 73 | "## Import nxneo4j\n", 74 | "\n", 75 | "If not already installed, install the latest version like this:" 76 | ] 77 | }, 78 | { 79 | "cell_type": "code", 80 | "execution_count": 115, 81 | "metadata": {}, 82 | "outputs": [ 83 | { 84 | "name": "stdout", 85 | "output_type": "stream", 86 | "text": [ 87 | "Requirement already satisfied: networkx-neo4j==0.0.2 in /opt/anaconda3/lib/python3.7/site-packages (0.0.2)\r\n", 88 | "Requirement already satisfied: neo4j-driver in /opt/anaconda3/lib/python3.7/site-packages (from networkx-neo4j==0.0.2) (4.0.0a4)\r\n", 89 | "Requirement already satisfied: pytz in /opt/anaconda3/lib/python3.7/site-packages (from neo4j-driver->networkx-neo4j==0.0.2) (2019.3)\r\n" 90 | ] 91 | } 92 | ], 93 | "source": [ 94 | "! pip install networkx-neo4j==0.0.2" 95 | ] 96 | }, 97 | { 98 | "cell_type": "markdown", 99 | "metadata": {}, 100 | "source": [ 101 | "Otherwise, follow:" 102 | ] 103 | }, 104 | { 105 | "cell_type": "code", 106 | "execution_count": 3, 107 | "metadata": {}, 108 | "outputs": [], 109 | "source": [ 110 | "import nxneo4j as nx" 111 | ] 112 | }, 113 | { 114 | "cell_type": "code", 115 | "execution_count": 4, 116 | "metadata": {}, 117 | "outputs": [], 118 | "source": [ 119 | "G = nx.Graph(driver)" 120 | ] 121 | }, 122 | { 123 | "cell_type": "code", 124 | "execution_count": 21, 125 | "metadata": {}, 126 | "outputs": [], 127 | "source": [ 128 | "G.delete_all() #BE CAREFUL! This will delete all the data.\n", 129 | " #By deleting, just making sure that the results are reprodusible." 130 | ] 131 | }, 132 | { 133 | "cell_type": "markdown", 134 | "metadata": {}, 135 | "source": [ 136 | "## Add Nodes" 137 | ] 138 | }, 139 | { 140 | "cell_type": "code", 141 | "execution_count": 22, 142 | "metadata": {}, 143 | "outputs": [], 144 | "source": [ 145 | "#Add a node\n", 146 | "G.add_node(1)" 147 | ] 148 | }, 149 | { 150 | "cell_type": "code", 151 | "execution_count": 23, 152 | "metadata": {}, 153 | "outputs": [], 154 | "source": [ 155 | "#Add multiple nodes as once\n", 156 | "G.add_nodes_from([1,2,3,4])" 157 | ] 158 | }, 159 | { 160 | "cell_type": "markdown", 161 | "metadata": {}, 162 | "source": [ 163 | "## Add Edges" 164 | ] 165 | }, 166 | { 167 | "cell_type": "code", 168 | "execution_count": 24, 169 | "metadata": {}, 170 | "outputs": [], 171 | "source": [ 172 | "#Add one edge\n", 173 | "G.add_edge(1,2)" 174 | ] 175 | }, 176 | { 177 | "cell_type": "code", 178 | "execution_count": 25, 179 | "metadata": {}, 180 | "outputs": [], 181 | "source": [ 182 | "#Add multiple edges\n", 183 | "G.add_edges_from([(1,2),(3,4)])" 184 | ] 185 | }, 186 | { 187 | "cell_type": "markdown", 188 | "metadata": {}, 189 | "source": [ 190 | "## Graph Data Science" 191 | ] 192 | }, 193 | { 194 | "cell_type": "code", 195 | "execution_count": 54, 196 | "metadata": {}, 197 | "outputs": [], 198 | "source": [ 199 | "G.delete_all()" 200 | ] 201 | }, 202 | { 203 | "cell_type": "code", 204 | "execution_count": 56, 205 | "metadata": {}, 206 | "outputs": [], 207 | "source": [ 208 | "G.load_twitter()" 209 | ] 210 | }, 211 | { 212 | "cell_type": "code", 213 | "execution_count": 57, 214 | "metadata": {}, 215 | "outputs": [], 216 | "source": [ 217 | "#You can change the default parameters like the following:\n", 218 | "G.identifier_property = 'username'\n", 219 | "G.relationship_type = 'FOLLOWS'\n", 220 | "G.node_label = 'User'" 221 | ] 222 | }, 223 | { 224 | "cell_type": "code", 225 | "execution_count": 82, 226 | "metadata": {}, 227 | "outputs": [ 228 | { 229 | "data": { 230 | "text/plain": [ 231 | "['markhneedham', 'nunenuh', 'businessinsider']" 232 | ] 233 | }, 234 | "execution_count": 82, 235 | "metadata": {}, 236 | "output_type": "execute_result" 237 | } 238 | ], 239 | "source": [ 240 | "nx.shortest_path(G, source='markhneedham', target='businessinsider')" 241 | ] 242 | }, 243 | { 244 | "cell_type": "code", 245 | "execution_count": 81, 246 | "metadata": {}, 247 | "outputs": [ 248 | { 249 | "data": { 250 | "text/plain": [ 251 | "[{'ng28softball'},\n", 252 | " {'frant_hartm',\n", 253 | " 'joebew42',\n", 254 | " '_JustinMoon_',\n", 255 | " 'antirez',\n", 256 | " 'LBacaj',\n", 257 | " 'mmetzger',\n", 258 | " 'ClhoHuerta',\n", 259 | " 'NickLuallin',\n", 260 | " 'PatOSullivanIBM',\n", 261 | " 'tomahock',\n", 262 | " 'fricau',\n", 263 | " 'PerWiklander',\n", 264 | " 'rautsan',\n", 265 | " 'ryantzj',\n", 266 | " 'Talend',\n", 267 | " 'develaper',\n", 268 | " 'rMdes_',\n", 269 | " 'rrrouyer',\n", 270 | " 'teoseller',\n", 271 | " 'noduslabs',\n", 272 | " 'alanlepo',\n", 273 | " 'Techforce1_nl',\n", 274 | " 'debugwand',\n", 275 | " 'h_oll',\n", 276 | " 'triptych',\n", 277 | " 'maker_iot_tr',\n", 278 | " 'DCI_Resources',\n", 279 | " 'alfonsodg',\n", 280 | " 'tahsin_mayeesha',\n", 281 | " 'irregularbi',\n", 282 | " 'clr_bnrd',\n", 283 | " 'JavaUnofficial',\n", 284 | " 'JeffreyAStewart',\n", 285 | " 'thoughtbot',\n", 286 | " 'kwyxz',\n", 287 | " 'jonathanhartsf',\n", 288 | " 'EzraSandzer',\n", 289 | " 'sldatacommunity',\n", 290 | " 'rimllr',\n", 291 | " 'brockjelmore',\n", 292 | " 'PatrickVMadden',\n", 293 | " 'TechedgeEs',\n", 294 | " 'Arzhanger',\n", 295 | " 'The_Zach_West',\n", 296 | " 'TomasKazmierski',\n", 297 | " 'rahulattuluri',\n", 298 | " 'doctor_cerulean',\n", 299 | " 'CluedInSeymour',\n", 300 | " 'h_ingo',\n", 301 | " 'dan_mcclary',\n", 302 | " 'mariuskarma',\n", 303 | " 'SokanAcademy',\n", 304 | " 'PieterCoussemen',\n", 305 | " 'Jc_ArtsCase',\n", 306 | " 'ShivaRe69882994',\n", 307 | " 'mags_ft',\n", 308 | " 'John_cena610',\n", 309 | " 'Loguteva',\n", 310 | " 'dripsandcastle',\n", 311 | " 'cottinstef',\n", 312 | " 'zodda',\n", 313 | " 'z3r0cool',\n", 314 | " 'TT_SemWeb',\n", 315 | " 'fzakariya',\n", 316 | " 's7ephen',\n", 317 | " 'Talenders',\n", 318 | " 'BeyondSearch',\n", 319 | " 'john_crocker',\n", 320 | " 'DannyCrichton',\n", 321 | " 'hackernewsfeed',\n", 322 | " 'InformatiqNews',\n", 323 | " 'MagemelloMario',\n", 324 | " 'kellyfj1',\n", 325 | " 'eoolsen',\n", 326 | " 'SeunMatt2',\n", 327 | " 'seldo',\n", 328 | " 'BLACKCERT',\n", 329 | " 'pwesterman',\n", 330 | " 'BrunoUngermann',\n", 331 | " 'satRdayParis',\n", 332 | " 'fblamanna',\n", 333 | " 'cmcd_phd',\n", 334 | " 'Fingent',\n", 335 | " 'akngw',\n", 336 | " 'AndrewFrater',\n", 337 | " 'SparsityTech',\n", 338 | " 'FrankVullers',\n", 339 | " 'oss_js',\n", 340 | " 'brianhurley',\n", 341 | " 'KMWorld',\n", 342 | " 'allangoode',\n", 343 | " 'dimoskoptsis',\n", 344 | " 'itscyberinject',\n", 345 | " 'arcreativenet',\n", 346 | " 'DavidNind',\n", 347 | " 'webpoint_new',\n", 348 | " 'GandhinagarNews',\n", 349 | " 'ItsMeijers',\n", 350 | " 'digitalenergyj',\n", 351 | " 'nickparsons',\n", 352 | " 'Kuujikai',\n", 353 | " 'raevilman',\n", 354 | " 'ashish_fagna',\n", 355 | " 'vastur',\n", 356 | " 'mirandakaywebb1',\n", 357 | " 'miguelinlas3',\n", 358 | " 'Cyril__',\n", 359 | " 'davidgasquez',\n", 360 | " 'blitznihar',\n", 361 | " 'GitHaiku',\n", 362 | " 'darshitj2',\n", 363 | " 'onlydatajobs',\n", 364 | " 'msbicoe',\n", 365 | " 'RMSite',\n", 366 | " 'thesilverlogic',\n", 367 | " 'tekiegirl',\n", 368 | " 'yennwolf',\n", 369 | " 'logly',\n", 370 | " 'BecomingDataSci',\n", 371 | " 'lilachmanheim',\n", 372 | " 'reseauloops',\n", 373 | " 'frankmcsherry',\n", 374 | " 'showmesolutions',\n", 375 | " 'StephanGils',\n", 376 | " 'stevedischinger',\n", 377 | " 'betterhn100',\n", 378 | " 'monah1711',\n", 379 | " 'kingrockie',\n", 380 | " 'appareto',\n", 381 | " 'codepitbull',\n", 382 | " 'alisovino',\n", 383 | " 'fabiogomezdiaz',\n", 384 | " 'carlesdijous',\n", 385 | " 'sqlenergy',\n", 386 | " 'bookwatchiprog',\n", 387 | " 'taswarbhatti',\n", 388 | " 'alexpospiech',\n", 389 | " 'rtehrani',\n", 390 | " 'krislerok',\n", 391 | " 'SFEIR',\n", 392 | " 'dstepp2',\n", 393 | " 'cddigitalorg',\n", 394 | " 'lulufrego',\n", 395 | " 'StevenXi07',\n", 396 | " 'menomeTech',\n", 397 | " 'Harkediansa',\n", 398 | " 'jbfavre',\n", 399 | " 'jackzimmerman',\n", 400 | " 'jamesharrison',\n", 401 | " 'TridzOnline',\n", 402 | " 'clojurenorth',\n", 403 | " 'waugoola',\n", 404 | " 'franse15',\n", 405 | " 'EricPBloom',\n", 406 | " 'aloksha',\n", 407 | " 'ElffarAnalytics',\n", 408 | " 'ShowHNDaily',\n", 409 | " 'carlosguadian',\n", 410 | " 'tistre',\n", 411 | " 'red_nodes',\n", 412 | " 'marie_alexb',\n", 413 | " 'ActianCorp',\n", 414 | " 'daye_nam',\n", 415 | " 'devtoolsdigest',\n", 416 | " 'ripienaar',\n", 417 | " 'hiteshgondalia',\n", 418 | " 'DavidSolesP',\n", 419 | " 'radityopw',\n", 420 | " 'Mareike2405',\n", 421 | " 'aureliengeorget',\n", 422 | " 'lukoerfer',\n", 423 | " 'bigdataparis',\n", 424 | " 'farashod',\n", 425 | " 'runnersbyte',\n", 426 | " 'tedvga',\n", 427 | " 'InfoProNetwork',\n", 428 | " 'interviewgig',\n", 429 | " 'DocQLio',\n", 430 | " 'CHardyG2',\n", 431 | " 'Harjunmaa',\n", 432 | " 'CLGAnalytics',\n", 433 | " 'SQL_Doch',\n", 434 | " 'WhaleHoss',\n", 435 | " 'chidambara09',\n", 436 | " 'Srijan',\n", 437 | " 'certosatweets',\n", 438 | " 'MarieGirardChop',\n", 439 | " 'M157q_News_RSS',\n", 440 | " 'realmgic',\n", 441 | " 'enova',\n", 442 | " 'ucfgeek',\n", 443 | " 'iGenomics',\n", 444 | " 'chefhans',\n", 445 | " 'ashdiscovers',\n", 446 | " '_ColinFay',\n", 447 | " 'kukharenko',\n", 448 | " 'quanderio',\n", 449 | " 'aaronzollman',\n", 450 | " 'sheislaurence',\n", 451 | " 'bk1_168',\n", 452 | " 'AvolutionAbacus',\n", 453 | " 'sumits_kumar',\n", 454 | " 'nikimari',\n", 455 | " 'alister_b',\n", 456 | " 'Ken_Yamamura',\n", 457 | " 'EbertFabi',\n", 458 | " 'Virtumente',\n", 459 | " 'danielcfng',\n", 460 | " 'RetweetedRajeev',\n", 461 | " 'jizaymes',\n", 462 | " 'hannes_lowette',\n", 463 | " 'Azn_CyberSleuth',\n", 464 | " 'CYxChris',\n", 465 | " 'HeyChelseaTroy',\n", 466 | " 'jmsunico',\n", 467 | " 'AnalyticExec',\n", 468 | " 'SwissCognitive',\n", 469 | " 'ldellaquila',\n", 470 | " 'jai_prasad17',\n", 471 | " 'stanleysuen',\n", 472 | " 'Pouncey17',\n", 473 | " 'willricketts',\n", 474 | " 'RupprechtMaria',\n", 475 | " 'dahowlett',\n", 476 | " 'niazjalal',\n", 477 | " 'Social_Cops',\n", 478 | " '0p4r3t0r',\n", 479 | " 'grep_kaustubh',\n", 480 | " 'MoneyhealthF',\n", 481 | " 'nelumihai',\n", 482 | " 'IdentityMonk',\n", 483 | " 'kevvurs',\n", 484 | " 'colinbarker19',\n", 485 | " 'Adron',\n", 486 | " 'techjunkiejh',\n", 487 | " 'startupsucht',\n", 488 | " 'GLEIF',\n", 489 | " 'Going_Digital30',\n", 490 | " 'DATANOMIQ',\n", 491 | " 'zkancs',\n", 492 | " 'patbaumgartner',\n", 493 | " 'amarghuman',\n", 494 | " 'GraphQLatSO',\n", 495 | " 'LucknowNews',\n", 496 | " 'pwsh_guy',\n", 497 | " 'NodeXL_Mktng',\n", 498 | " 'metaincognito',\n", 499 | " 'CecileHbh',\n", 500 | " 'l1formaticien',\n", 501 | " 'bjoernoest',\n", 502 | " 'certifiedwaif',\n", 503 | " 'namerson5',\n", 504 | " 'BenListyg',\n", 505 | " 'LisaAnneBraun',\n", 506 | " 'jimwebber',\n", 507 | " 'turley714',\n", 508 | " 'davib0',\n", 509 | " 'kvasnica',\n", 510 | " 'HalbaradKenafin',\n", 511 | " 'andigutmans',\n", 512 | " 'AsISeeTech',\n", 513 | " 'luiy',\n", 514 | " 'corbanb',\n", 515 | " 'leviw',\n", 516 | " 'nschaetti',\n", 517 | " 'AndrewMBaker',\n", 518 | " 'euranova',\n", 519 | " 'gscalingacademy',\n", 520 | " 'maruyama3',\n", 521 | " 'floorter',\n", 522 | " 'JonnyDubowsky',\n", 523 | " 'dutradotdev',\n", 524 | " 'aschauerhuber',\n", 525 | " 'brunoborges',\n", 526 | " 'VizMatt',\n", 527 | " 'JonoHeher',\n", 528 | " 'noservershere',\n", 529 | " 'ira_res',\n", 530 | " 'dariospagnolo',\n", 531 | " 'Indiaitfirms',\n", 532 | " 'CoreDumpConf',\n", 533 | " 'andrewbain',\n", 534 | " 'ai_community',\n", 535 | " 'SQLDoubleG',\n", 536 | " 'maxdalmas',\n", 537 | " 'DvKlopfenstein',\n", 538 | " 'w0nvel',\n", 539 | " 'sadsaviour',\n", 540 | " 'CMR_ExecAdv',\n", 541 | " 'Decideo',\n", 542 | " 'michvictor',\n", 543 | " 'herahussain',\n", 544 | " 'ziniman',\n", 545 | " 'paulmiller99',\n", 546 | " 'ThomasG77',\n", 547 | " 'grapheverywhere',\n", 548 | " 'ykarikos',\n", 549 | " 'solidbrokers',\n", 550 | " 'xagronaut',\n", 551 | " 'mySocializerHub',\n", 552 | " 'GraphXr',\n", 553 | " 'IAP_Networking',\n", 554 | " 'iange',\n", 555 | " 'MailpicksU',\n", 556 | " 'OutCoastIt',\n", 557 | " 'b0rb0SS',\n", 558 | " 'ronald_istos',\n", 559 | " 'jk8172',\n", 560 | " 'SRoyLee',\n", 561 | " 'Webseo69',\n", 562 | " 'hpdailyrant',\n", 563 | " 'CourseGift',\n", 564 | " 'jugsummercamp',\n", 565 | " 'GQAdonisCTO',\n", 566 | " 'nmcl',\n", 567 | " 'nthnryn',\n", 568 | " 'Manali_MTS',\n", 569 | " 'batoolchishti92',\n", 570 | " 'simondachstr',\n", 571 | " 'Desk_Rider',\n", 572 | " 'agentGav',\n", 573 | " 'stevenamapes',\n", 574 | " 'KoyO_JakaNeEs',\n", 575 | " 'danielg0ldberg',\n", 576 | " 'Azure',\n", 577 | " 'mcohmi',\n", 578 | " 'JeremyCoxCAE',\n", 579 | " 'NYDF_Platform',\n", 580 | " 'c_z',\n", 581 | " 'doggoit',\n", 582 | " 'dvirsky',\n", 583 | " 'JeremyMorrell',\n", 584 | " 'etsinfupv',\n", 585 | " 'SeqComplete',\n", 586 | " 'mcvey_greg',\n", 587 | " 'AyittahBernard',\n", 588 | " 'swagunke',\n", 589 | " 'DinisCruz',\n", 590 | " 'JamesFlint',\n", 591 | " 'kasia_git',\n", 592 | " 'bigdata',\n", 593 | " 'Surgisse',\n", 594 | " 'anteos',\n", 595 | " 'Antonio23132075',\n", 596 | " 'StackDevJobs',\n", 597 | " 'j3mbe',\n", 598 | " 'statsprovenutn',\n", 599 | " 'blm849',\n", 600 | " 'ArchBeatDev',\n", 601 | " 'arlynculwick',\n", 602 | " 'GA',\n", 603 | " 'DrStrange_Bot',\n", 604 | " 'node_stack',\n", 605 | " 'MGazanayi',\n", 606 | " 'learnNoSql',\n", 607 | " 'BinaryMuse',\n", 608 | " 'StepsizeHQ',\n", 609 | " 'Marc_Pou',\n", 610 | " 'ChrisKa52931839',\n", 611 | " 'AcaymoNM',\n", 612 | " 'pgxn',\n", 613 | " 'davidrapin',\n", 614 | " 'minhphien',\n", 615 | " 'lordmj',\n", 616 | " 'msgDAVID',\n", 617 | " 'mtmac4',\n", 618 | " 'Steve4years',\n", 619 | " 'hackintoshrao',\n", 620 | " 'scikit_yb',\n", 621 | " 'T0mmySk',\n", 622 | " 'Instacart',\n", 623 | " 'robertopuyo',\n", 624 | " 'Edopeno',\n", 625 | " 'nfigay',\n", 626 | " 'erictummers',\n", 627 | " 'PetroInnovation',\n", 628 | " 'roens',\n", 629 | " 'Neo4jGH',\n", 630 | " 'synyx_ka',\n", 631 | " 'therealdudez',\n", 632 | " 'DigiTechNewsNet',\n", 633 | " 'cb0day',\n", 634 | " 'sdeterme',\n", 635 | " 'elixirforum',\n", 636 | " 'vikrambodke09',\n", 637 | " 'TSchuermans',\n", 638 | " 'st3llasia',\n", 639 | " 'RezaC1',\n", 640 | " 'senzing',\n", 641 | " 'arpit',\n", 642 | " 'AppsCodeHQ',\n", 643 | " 'kirkham_anthony',\n", 644 | " '_wald0',\n", 645 | " 'AlaMenai',\n", 646 | " 'stefandumont',\n", 647 | " 'sysbus_eu',\n", 648 | " 'DammianMiller',\n", 649 | " 'wangsuya1',\n", 650 | " 'BudapestData',\n", 651 | " 'geeks_db',\n", 652 | " 'jamescummings',\n", 653 | " 'ICBmunich',\n", 654 | " 'samuelroze',\n", 655 | " 'InformaticaC1',\n", 656 | " 'wilsonkriegel',\n", 657 | " 'JetTechnology',\n", 658 | " 'profanegeometry',\n", 659 | " 'Streak',\n", 660 | " 'AlleyWatch',\n", 661 | " 'Python_Links',\n", 662 | " 'daniel_sellers',\n", 663 | " 'manishrjain',\n", 664 | " 'Sheridan_Gerard',\n", 665 | " 'brightopare',\n", 666 | " 'odbmsorg',\n", 667 | " 'munsteriron',\n", 668 | " 'zdnetfr',\n", 669 | " 'LarifariNerdy',\n", 670 | " 'plzbeemyfriend',\n", 671 | " 'TheCloudSumo',\n", 672 | " 'freiksenet',\n", 673 | " 'chpanto',\n", 674 | " 'highscal',\n", 675 | " 'kaisrei',\n", 676 | " 'FroehlichMarcel',\n", 677 | " 'csirac2',\n", 678 | " 'madewithtea',\n", 679 | " 'iamrahuljain_in',\n", 680 | " 'siliconion',\n", 681 | " 'PythonLoop',\n", 682 | " 'RobSeder',\n", 683 | " 'hyperdev_fr',\n", 684 | " 'Real_Time_Data',\n", 685 | " 'rags080484',\n", 686 | " 'PortoData',\n", 687 | " 'PHPStack',\n", 688 | " 'devops_chat',\n", 689 | " 'OntotextGraphDB',\n", 690 | " 'gitbisect',\n", 691 | " 'MobilesAppStore',\n", 692 | " 'RashmiNbr',\n", 693 | " 'westendoerpf',\n", 694 | " 'iam_vee',\n", 695 | " 'AndreasKuczera',\n", 696 | " 'jaedenlove',\n", 697 | " 'HacksHackersBA',\n", 698 | " 'cheeaun',\n", 699 | " 'insight_socio',\n", 700 | " 'eric_kavanagh',\n", 701 | " 'sebclick',\n", 702 | " 'nicokosi',\n", 703 | " 'swinkelstom',\n", 704 | " 'Bh26',\n", 705 | " 'marchoutgraaf',\n", 706 | " 'lordofmisrule',\n", 707 | " 'timanderson',\n", 708 | " 'JRayDenver',\n", 709 | " 'nycallday247',\n", 710 | " 'project_network',\n", 711 | " 'thramp',\n", 712 | " 'KC72576_Tech',\n", 713 | " 'paper_radio',\n", 714 | " 'ardan7779',\n", 715 | " 'Rache1H',\n", 716 | " 'louis_guitton',\n", 717 | " 'iot_ng',\n", 718 | " 'jsalsman',\n", 719 | " 'nabilblk',\n", 720 | " '_carpenter315',\n", 721 | " 'uber1geek',\n", 722 | " 'mdavidallen',\n", 723 | " 'JAdP',\n", 724 | " 'HarishMinions20',\n", 725 | " 'usepanda',\n", 726 | " 'SeattlesBanker',\n", 727 | " 'oldaily',\n", 728 | " 'node_developer',\n", 729 | " 'binarymachines',\n", 730 | " 'contentspd1',\n", 731 | " 'scottakenhead',\n", 732 | " 'WorkgridSoft',\n", 733 | " 'mscavazzin',\n", 734 | " 'DanielGallagher',\n", 735 | " 'boldingbroke',\n", 736 | " 'makmanalp',\n", 737 | " 'nvfontoy',\n", 738 | " 'jodok',\n", 739 | " 'artbrock',\n", 740 | " 'Neo4jCommunityQ',\n", 741 | " 'fRoldanCordoba',\n", 742 | " 'TimWilliate',\n", 743 | " 'ThatSSR',\n", 744 | " 'nodebuster',\n", 745 | " 'HunterSoluk',\n", 746 | " '2linuxorg',\n", 747 | " 'basarat',\n", 748 | " 'shivkumarganesh',\n", 749 | " 'PToschka',\n", 750 | " 'semwebcompany',\n", 751 | " 'JosephPitluck',\n", 752 | " 'jaume_olledo',\n", 753 | " 'SQLShark',\n", 754 | " 'amt_jose',\n", 755 | " 'mfauscette',\n", 756 | " 'NovasTaylor',\n", 757 | " 'rstiles16',\n", 758 | " 'SamiraKorani',\n", 759 | " 'stonerichio',\n", 760 | " 'YasserIM',\n", 761 | " 'wednesday099',\n", 762 | " 'CVEnew',\n", 763 | " 'itfmco',\n", 764 | " 'ohubaut',\n", 765 | " 'pdxleif',\n", 766 | " 'learnlinksfeed',\n", 767 | " 'database_camp',\n", 768 | " 'SiGe92',\n", 769 | " 'drimcalban',\n", 770 | " 'cleishm',\n", 771 | " 'jmjhjr',\n", 772 | " 'tek_news',\n", 773 | " 'HEbertKONLABS',\n", 774 | " 'eliaswalyba',\n", 775 | " 'jgxdot',\n", 776 | " 'sypherlev',\n", 777 | " 'cteodor',\n", 778 | " 'werowe',\n", 779 | " 'blubbfiction',\n", 780 | " 'duward',\n", 781 | " 'IanMmmm',\n", 782 | " 'clojuredconf',\n", 783 | " 'dee_bloo',\n", 784 | " 'JavaScript_Plow',\n", 785 | " 'smniemi',\n", 786 | " 'meherfalcon',\n", 787 | " 'PinPopular',\n", 788 | " 'MattWilcox',\n", 789 | " 'Stasoni',\n", 790 | " 'abhishekkgupta',\n", 791 | " 'datao',\n", 792 | " 'The_Code_Shirts',\n", 793 | " 'dspsingh51',\n", 794 | " 'StachuDotNet',\n", 795 | " 'jpfersich',\n", 796 | " 'XLinStrategy',\n", 797 | " 'MohitShrestha',\n", 798 | " 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'gaku_hiro',\n", 1316 | " 'grimrose',\n", 1317 | " 'haccht',\n", 1318 | " 'harryharries861',\n", 1319 | " 'hi86074659',\n", 1320 | " 'him0net',\n", 1321 | " 'hiromoon_428',\n", 1322 | " 'hoishinxii',\n", 1323 | " 'hrk0619',\n", 1324 | " 'i_szyn',\n", 1325 | " 'ike_dai',\n", 1326 | " 'insomnyan',\n", 1327 | " 'isyumi_net',\n", 1328 | " 'iw_tatsu',\n", 1329 | " 'janome_s',\n", 1330 | " 'jbking',\n", 1331 | " 'jdoiwork',\n", 1332 | " 'jedipunkz',\n", 1333 | " 'johtani',\n", 1334 | " 'juntoku_y',\n", 1335 | " 'junya',\n", 1336 | " 'jwhaco',\n", 1337 | " 'ka_ka_xyz',\n", 1338 | " 'kabukawa',\n", 1339 | " 'kbaba1001',\n", 1340 | " 'keii1111',\n", 1341 | " 'keke_moto',\n", 1342 | " 'keyhs8',\n", 1343 | " 'kimukou2628',\n", 1344 | " 'kimutansk',\n", 1345 | " 'kommy_mlnx',\n", 1346 | " 'komo_fr',\n", 1347 | " 'kompiro',\n", 1348 | " 'kouwasi',\n", 1349 | " 'koyakei',\n", 1350 | " 'koyamauchi',\n", 1351 | " 'kuwa_tw',\n", 1352 | " 'kymmt90',\n", 1353 | " 'letusfly85',\n", 1354 | " 'linyows',\n", 1355 | " 'lll_anna_lll',\n", 1356 | " 'm4buya',\n", 1357 | " 'ma2saka',\n", 1358 | " 'maato',\n", 1359 | " 'matsuu_zatsu',\n", 1360 | " 'matt_zeus',\n", 1361 | " 'mininobu',\n", 1362 | " 'misakapon',\n", 1363 | " 'mitsuhiro',\n", 1364 | " 'miyamo_madoka',\n", 1365 | " 'monochromegane',\n", 1366 | " 'morihaya55',\n", 1367 | " 'moririn9803',\n", 1368 | " 'moris5321',\n", 1369 | " 'moznion',\n", 1370 | " 'mya_ake',\n", 1371 | " 'narutaro',\n", 1372 | " 'nikochan2k',\n", 1373 | " 'niwatolli3',\n", 1374 | " 'nnao45',\n", 1375 | " 'nntsugu',\n", 1376 | " 'nullue',\n", 1377 | " 'ok_mozy',\n", 1378 | " 'ora_club',\n", 1379 | " 'oraryotas',\n", 1380 | " 'oss_info',\n", 1381 | " 'pg_son',\n", 1382 | " 'prmnmbr_',\n", 1383 | " 'qb0C80aE',\n", 1384 | " 'rinosamakanata',\n", 1385 | " 'rr250r_smr',\n", 1386 | " 'ryoasu_107',\n", 1387 | " 's01',\n", 1388 | " 'sakapun',\n", 1389 | " 'sect10n9',\n", 1390 | " 'sh_0i3n07',\n", 1391 | " 'shiget84',\n", 1392 | " 'shin_tsujido',\n", 1393 | " 'shinpei0213',\n", 1394 | " 'shiraponsu',\n", 1395 | " 'shirara1',\n", 1396 | " 'smdmts',\n", 1397 | " 'ssssssigma',\n", 1398 | " 'stepan_ve',\n", 1399 | " 'stereocat',\n", 1400 | " 'substance626',\n", 1401 | " 'suke083',\n", 1402 | " 'syossan27',\n", 1403 | " 't_marcus87',\n", 1404 | " 'taisyoku_P',\n", 1405 | " 'take3000',\n", 1406 | " 'takeokunn',\n", 1407 | " 'takezoen',\n", 1408 | " 'tatakaba',\n", 1409 | " 'tech_advent',\n", 1410 | " 'tech_slideshare',\n", 1411 | " 'toricls',\n", 1412 | " 'tttamurat',\n", 1413 | " 'tty_tkhs_ml',\n", 1414 | " 'uehaj',\n", 1415 | " 'utsumuki_neko',\n", 1416 | " 'vaaaaanquish',\n", 1417 | " 'water_boy_tokyo',\n", 1418 | " 'wereyak',\n", 1419 | " 'yasushia',\n", 1420 | " 'yokatsuki',\n", 1421 | " 'yoshidashingo',\n", 1422 | " 'yosukehara',\n", 1423 | " 'ysnr_ksm',\n", 1424 | " 'yujiorama',\n", 1425 | " 'yupeji',\n", 1426 | " 'yurau75',\n", 1427 | " 'yuw27b',\n", 1428 | " 'zabbiozabbio'},\n", 1429 | " {'martinj06231870'},\n", 1430 | " {'DiscoveryMosti'},\n", 1431 | " {'9yen'},\n", 1432 | " {'TopTechSolution'},\n", 1433 | " {'fmeglar'},\n", 1434 | " {'composioco'},\n", 1435 | " {'RmwVamp'},\n", 1436 | " {'andre_andre_007'},\n", 1437 | " {'fbajri'},\n", 1438 | " {'aishulakshmi'},\n", 1439 | " {'DMZAsiaPacific', 'GARSmailes', 'PlusCoaching', 'RaedanExchange', 'csanki'},\n", 1440 | " {'hackernews100'},\n", 1441 | " {'JabalpurChronic'},\n", 1442 | " {'OneTwoFreee'},\n", 1443 | " {'GurgaonS'},\n", 1444 | " {'RhodeIslandChro'},\n", 1445 | " {'SmortiWalter'},\n", 1446 | " {'1Gerbs',\n", 1447 | " 'Dave_Jonez_02',\n", 1448 | " 'DevNullProd',\n", 1449 | " 'HammerToe',\n", 1450 | " 'Hodor',\n", 1451 | " 'KevinKing64',\n", 1452 | " 'MikeGlobTrends',\n", 1453 | " 'Ocean_4549',\n", 1454 | " 'Pavel_Escobar86',\n", 1455 | " 'Prosatan666',\n", 1456 | " 'XRPMedia',\n", 1457 | " 'XRP_121',\n", 1458 | " 'XRPeezy',\n", 1459 | " 'XrpCenter',\n", 1460 | " 'akrepisback',\n", 1461 | " 'centurypointllc',\n", 1462 | " 'luv2hodl',\n", 1463 | " 'mindclimbing',\n", 1464 | " 'thompsanator',\n", 1465 | " 'xrpscan'},\n", 1466 | " {'doriath69'},\n", 1467 | " {'Wunderkidblog'},\n", 1468 | " {'_ericlimao'},\n", 1469 | " {'asdrgil'},\n", 1470 | " {'akiatoji'},\n", 1471 | " {'WorkCaryNC'},\n", 1472 | " {'TacticalTokens'},\n", 1473 | " {'amna_hira'},\n", 1474 | " {'_powerbuilder'},\n", 1475 | " {'AlchemySki'},\n", 1476 | " {'PhynoMyMentor'},\n", 1477 | " {'csabiu'},\n", 1478 | " {'BareillyMail'},\n", 1479 | " {'360WiseMedia'},\n", 1480 | " {'DigitalPsyche', 'RareButSerious'},\n", 1481 | " {'MCBE_Portal', 'ecila_vip', 'kazuemon_0602'},\n", 1482 | " {'IFFCNL'},\n", 1483 | " {'yodirkx'},\n", 1484 | " {'Darren_Mann'},\n", 1485 | " {'Gizlydeals'},\n", 1486 | " {'plwarre'},\n", 1487 | " {'bihranalytics'},\n", 1488 | " {'DominikLincer'},\n", 1489 | " {'righelp'},\n", 1490 | " {'Nagaland_News1'},\n", 1491 | " {'eroznama'},\n", 1492 | " {'HowrahNewsToday'},\n", 1493 | " {'CheesecakeLabs'},\n", 1494 | " {'Jiangning16'},\n", 1495 | " {'sameershelavale'},\n", 1496 | " {'WildeGio'},\n", 1497 | " {'SassyBDenise'},\n", 1498 | " {'klaasheek'},\n", 1499 | " {'hakandamar'},\n", 1500 | " {'natarajsasid'},\n", 1501 | " {'tigran_tv'},\n", 1502 | " {'rahularyan786'},\n", 1503 | " {'matsurisanv2'},\n", 1504 | " {'edgarmontalvo76'},\n", 1505 | " {'TomNwainwright'},\n", 1506 | " {'Mutiarasoftindo'},\n", 1507 | " {'am9gw'},\n", 1508 | " {'rzmpro'},\n", 1509 | " {'GoogleAlerts1'},\n", 1510 | " {'VijayS91403818'},\n", 1511 | " {'WisdomjobsH'},\n", 1512 | " {'anishgrandhi'},\n", 1513 | " {'btMatthew'},\n", 1514 | " {'VirineyaMr'},\n", 1515 | " {'825jp'},\n", 1516 | " {'ales85943804'},\n", 1517 | " {'frontendbook'},\n", 1518 | " {'cloud502'},\n", 1519 | " {'ignotamedia'},\n", 1520 | " {'livenewsstockma'},\n", 1521 | " {'NascarByJava'},\n", 1522 | " {'deadmilkman'},\n", 1523 | " {'im_not_enemy'},\n", 1524 | " {'ztztech'},\n", 1525 | " {'dot_Ri4'},\n", 1526 | " {'adamjennison'},\n", 1527 | " {'fusedat'},\n", 1528 | " {'malaybhatta'},\n", 1529 | " {'FuhStudium'},\n", 1530 | " {'RichmondNewsNow'},\n", 1531 | " {'soxamarin'},\n", 1532 | " {'MikeTheAggieKID'},\n", 1533 | " {'MandelaCS'},\n", 1534 | " {'ImFanky'},\n", 1535 | " {'King_Sloth95', 'liayeaaah', 'yasabdulkadir'},\n", 1536 | " {'iForexRobot'},\n", 1537 | " {'justinsmorgan'},\n", 1538 | " {'LudoLyon'},\n", 1539 | " {'codeqa_ja', 'codeqa_ko'},\n", 1540 | " {'ahmadajeb'},\n", 1541 | " {'Picante_Media'},\n", 1542 | " {'California_hr'},\n", 1543 | " {'EmploiBruxelles'},\n", 1544 | " {'nieuwejobs'},\n", 1545 | " {'saeedeldah'},\n", 1546 | " {'CanGovSciPubs'},\n", 1547 | " {'vdhJonas'},\n", 1548 | " {'sysdevmemor'},\n", 1549 | " {'senthil_hi'},\n", 1550 | " {'MoleBizMedia'},\n", 1551 | " {'typenullbot'},\n", 1552 | " {'Ebooksources1'},\n", 1553 | " {'newsvire1'},\n", 1554 | " {'JoostEaston'},\n", 1555 | " {'imohorianu'},\n", 1556 | " {'calorinesteps'},\n", 1557 | " {'Cekingx'},\n", 1558 | " {'God14Peace'},\n", 1559 | " {'boxong84', 'tinysong84'},\n", 1560 | " {'athul_karthik'},\n", 1561 | " {'FileMaker',\n", 1562 | " 'FileMakerToday',\n", 1563 | " 'FileMaker_NL',\n", 1564 | " 'JeroenAarts',\n", 1565 | " 'JorisAarts',\n", 1566 | " 'ShawnGillisAFFI',\n", 1567 | " 'agir',\n", 1568 | " 'att_it_ude',\n", 1569 | " 'clickworks_eu',\n", 1570 | " 'didierdaglinckx',\n", 1571 | " 'douglasalder',\n", 1572 | " 'fmsummit',\n", 1573 | " 'hedmanjohan77',\n", 1574 | " 'tcolles73',\n", 1575 | " 'vmenanno'},\n", 1576 | " {'ABNewswire'},\n", 1577 | " {'Whaley9ja'},\n", 1578 | " {'serebralfunding'},\n", 1579 | " {'threatmeter'},\n", 1580 | " {'GodColComputing'},\n", 1581 | " {'SaberX01'},\n", 1582 | " {'Sir_FredBanting'},\n", 1583 | " {'techstartupguy'},\n", 1584 | " {'EasternIndiaNew'},\n", 1585 | " {'Danomitez'},\n", 1586 | " {'aankitmishraa'},\n", 1587 | " {'MeerutReporter'},\n", 1588 | " {'EricMacLeod920'},\n", 1589 | " {'porkbelly369'},\n", 1590 | " {'StormMela'},\n", 1591 | " {'ttttt'},\n", 1592 | " {'isamarsa71'},\n", 1593 | " {'GayaHerald'},\n", 1594 | " {'ExploringBlock'},\n", 1595 | " {'HackerfallFeed'},\n", 1596 | " {'Osukarin'},\n", 1597 | " {'thekuwar'},\n", 1598 | " {'newstermer'},\n", 1599 | " {'rijin1230609'},\n", 1600 | " {'inventivaindia'},\n", 1601 | " {'HomesteadDev'},\n", 1602 | " {'MizoramMail'},\n", 1603 | " {'SecurityNews'},\n", 1604 | " {'WeekServices'},\n", 1605 | " {'sgroenendal'},\n", 1606 | " {'DaraWehmeyer'},\n", 1607 | " {'repo_go'},\n", 1608 | " {'magrid_prisca'},\n", 1609 | " {'JobOfferUSA'},\n", 1610 | " {'thedpsadvisors'},\n", 1611 | " {'michael_l_heuer'},\n", 1612 | " {'aspnetissues', 'dotnetissues'},\n", 1613 | " {'RPubsHotEntry', 'RPubsRecent'},\n", 1614 | " {'fancyfaceza'},\n", 1615 | " {'wqv5mbs14'},\n", 1616 | " {'Alastairkretser'},\n", 1617 | " {'JKHeadlines'},\n", 1618 | " {'Saikat475k'},\n", 1619 | " {'softnix'},\n", 1620 | " {'moleytc'},\n", 1621 | " {'LesCNow'},\n", 1622 | " {'trendnewsdot'},\n", 1623 | " {'OnlineInstruct1'},\n", 1624 | " {'AlexaAvitto'},\n", 1625 | " {'freelanceOffres'},\n", 1626 | " {'Brizzysport'},\n", 1627 | " {'NewDelhiNews1'},\n", 1628 | " {'cguija'},\n", 1629 | " {'Babaranwar9'},\n", 1630 | " {'TechNewsexpert'},\n", 1631 | " {'Rajasthan_NP'},\n", 1632 | " {'SnaptechNews'},\n", 1633 | " {'m_dubbs'},\n", 1634 | " {'ZwoSchlagzeilen'},\n", 1635 | " {'kitman_yiu'},\n", 1636 | " {'adeosecurity'},\n", 1637 | " {'sbmelton'},\n", 1638 | " {'jaorr95'},\n", 1639 | " {'The_one_E'},\n", 1640 | " {'Jamar_100'},\n", 1641 | " {'SwissBankMane'},\n", 1642 | " {'fiweh'},\n", 1643 | " {'Stonewater20'},\n", 1644 | " {'redfields'},\n", 1645 | " {'Swahilipages'},\n", 1646 | " {'Crystal_R_Brodi', 'ledrew'},\n", 1647 | " {'AlisonCYoung88'},\n", 1648 | " {'Eroskhan1211'},\n", 1649 | " {'siobhansabino'},\n", 1650 | " {'Semtori'},\n", 1651 | " {'httjinsoul'},\n", 1652 | " {'purelyfast'},\n", 1653 | " {'financialbuzz'},\n", 1654 | " {'99udemy'},\n", 1655 | " {'AjithVerma'},\n", 1656 | " {'MENA_Britain'},\n", 1657 | " {'RankZoom'},\n", 1658 | " {'Saliga10'},\n", 1659 | " {'haunthaus'},\n", 1660 | " {'vivasasvegas'},\n", 1661 | " {'DineshMaheshwri'},\n", 1662 | " {'lozhn'},\n", 1663 | " {'RPQ48'},\n", 1664 | " {'iangodman'},\n", 1665 | " {'bootusb1'},\n", 1666 | " {'ParicioRafa'},\n", 1667 | " {'CrosswaterJob'},\n", 1668 | " {'42clientsTim'},\n", 1669 | " {'ojcst_journal'},\n", 1670 | " {'freelancingphil'},\n", 1671 | " {'lingamarlas'},\n", 1672 | " {'lieberman__'},\n", 1673 | " {'PansehTsewole1'},\n", 1674 | " {'latestcanada'},\n", 1675 | " {'bfkese'},\n", 1676 | " {'WorkLynnMA'},\n", 1677 | " {'timaccenture'},\n", 1678 | " {'CecileRay', 'NgoImpact'},\n", 1679 | " {'seeklogo'},\n", 1680 | " {'KenyanTraffic', 'ThikaTowntoday', 'at254Kenya'},\n", 1681 | " {'wowebookorg'},\n", 1682 | " {'NewsMadurai'},\n", 1683 | " {'wildrot'},\n", 1684 | " {'BeerbliotecaApp'},\n", 1685 | " {'SpiritOfTheJag'},\n", 1686 | " {'christianebuddy'},\n", 1687 | " {'Lifina'},\n", 1688 | " {'pls_ss'},\n", 1689 | " {'WtmMao'},\n", 1690 | " {'Stock_Market_Pr', 'feed_stocks'},\n", 1691 | " {'freshgoodsuk'},\n", 1692 | " {'chrisdpeters'},\n", 1693 | " {'bygcho'},\n", 1694 | " {'AirdropsCoin1'},\n", 1695 | " {'TwkalC'},\n", 1696 | " {'danielzairick'},\n", 1697 | " {'marimoaki'},\n", 1698 | " {'APHeadlines1'},\n", 1699 | " {'Node_Geek'},\n", 1700 | " {'garyskeete'},\n", 1701 | " {'ollorinko'},\n", 1702 | " {'GlobeTechReport'},\n", 1703 | " {'GiridihJournal'},\n", 1704 | " {'PerpustakaanITS'},\n", 1705 | " {'rabah_wael'},\n", 1706 | " {'manishti2004'},\n", 1707 | " {'SlicesSLU'},\n", 1708 | " {'RandWikipediaFr'},\n", 1709 | " {'youngs_suh'},\n", 1710 | " {'psclgllt'},\n", 1711 | " {'fsudmann'},\n", 1712 | " {'janisreading'},\n", 1713 | " {'LifewithAI'},\n", 1714 | " {'TechNewsFast'},\n", 1715 | " {'lontchi'},\n", 1716 | " {'oolpublishing'},\n", 1717 | " {'CookMyProject'},\n", 1718 | " {'websiteprousa'},\n", 1719 | " {'hdyhrosli'},\n", 1720 | " {'windenergysci'},\n", 1721 | " {'pandyavaibh'},\n", 1722 | " {'ingridcervin'},\n", 1723 | " {'CGRBOregonState'},\n", 1724 | " {'CloudReputation'},\n", 1725 | " {'BilaspurNewsFla'},\n", 1726 | " {'newsv24'},\n", 1727 | " {'blendedio'},\n", 1728 | " {'Photoshop__Tut'},\n", 1729 | " {'PhreeTechltd'},\n", 1730 | " {'feedpushr'},\n", 1731 | " {'fabianito63'},\n", 1732 | " {'BigSeanSSB'},\n", 1733 | " {'FrancoLisi'},\n", 1734 | " {'Dila66294955'},\n", 1735 | " {'Brainstrust_HQ'},\n", 1736 | " {'1Wisdomjobs'},\n", 1737 | " {'jazzdrummer420'},\n", 1738 | " {'yeti_in_a_box'},\n", 1739 | " {'getvantagepoint'},\n", 1740 | " {'thesunshinerepo'},\n", 1741 | " {'BigDataSpace'},\n", 1742 | " {'McCartn_ebooks'},\n", 1743 | " {'brussels_event'},\n", 1744 | " {'vo3xel'},\n", 1745 | " {'ReeceOBryan'},\n", 1746 | " {'douglasanalista'},\n", 1747 | " {'cichy_de'},\n", 1748 | " {'Angelic83007262'},\n", 1749 | " {'kchalise'},\n", 1750 | " {'kikuzone'},\n", 1751 | " {'shrideepghogare'},\n", 1752 | " {'RoberttBertton'},\n", 1753 | " {'kentfordev'},\n", 1754 | " {'Juliaat05'},\n", 1755 | " {'AurangabadMagaz'},\n", 1756 | " {'CrweWorld'},\n", 1757 | " {'NYUSternFC'},\n", 1758 | " {'pol_ins'},\n", 1759 | " {'corywcordell'},\n", 1760 | " {'Toshakins'},\n", 1761 | " {'myassirullah'},\n", 1762 | " {'isbc_rus'},\n", 1763 | " {'prabhinmp'},\n", 1764 | " {'DarkCreekWay'},\n", 1765 | " {'micheseco'},\n", 1766 | " {'tvalentenc'},\n", 1767 | " {'visceralnair'},\n", 1768 | " {'BharatDaily'},\n", 1769 | " {'storewithcoupon'},\n", 1770 | " {'JobzBot'},\n", 1771 | " {'avhk47'},\n", 1772 | " {'Flip101420'},\n", 1773 | " {'AmricaNascimen2'},\n", 1774 | " {'arrowsmith'},\n", 1775 | " {'anitha_perumal'},\n", 1776 | " {'Dsanwoola'},\n", 1777 | " {'fcggamou'},\n", 1778 | " {'PhoNetworks'},\n", 1779 | " {'Autotestdrivers'},\n", 1780 | " {'knbzyh'},\n", 1781 | " {'ProfessorFlossy'},\n", 1782 | " {'kaisarhasansohe'},\n", 1783 | " {'pcs31493'},\n", 1784 | " {'AnkaaEngine'},\n", 1785 | " {'A_Zbookstore'},\n", 1786 | " {'azharjamal'},\n", 1787 | " {'f10ck3'},\n", 1788 | " {'ImranOnline_net'},\n", 1789 | " {'techrdv'},\n", 1790 | " {'lb_greens'},\n", 1791 | " {'BrentonPoke'},\n", 1792 | " {'jsringo'},\n", 1793 | " {'travislepp'},\n", 1794 | " {'pmiozzi'},\n", 1795 | " {'hacklinesapp'},\n", 1796 | " {'DevelopKitchen'},\n", 1797 | " {'JohnAngel1977'},\n", 1798 | " {'DeepFinds'},\n", 1799 | " {'itlize'},\n", 1800 | " {'vruchtvet'},\n", 1801 | " {'abdellah19922'},\n", 1802 | " {'gautamkalal'},\n", 1803 | " {'NoidaChronicle'},\n", 1804 | " {'it_malibu'},\n", 1805 | " {'alphainspire'},\n", 1806 | " {'stargao3'},\n", 1807 | " {'facttob'},\n", 1808 | " {'aaaa888824'},\n", 1809 | " {'pvmarketing'},\n", 1810 | " {'sanopoyo'},\n", 1811 | " {'Xpacer_'},\n", 1812 | " {'Geezwild'},\n", 1813 | " {'ghafoor2018'},\n", 1814 | " {'InfoManipur'},\n", 1815 | " {'Hauxon'},\n", 1816 | " {'CrmJoy'},\n", 1817 | " {'dearappauthors'},\n", 1818 | " {'ajdiangelus'},\n", 1819 | " {'tambovcev99'},\n", 1820 | " {'marketnewslates'},\n", 1821 | " {'DumaOctavian'},\n", 1822 | " {'daniellam83'},\n", 1823 | " {'DiabetesTweets'},\n", 1824 | " {'JCI_SecuritySME'},\n", 1825 | " {'BetDollars'},\n", 1826 | " {'NTKRNow'},\n", 1827 | " {'jobs_in_boston'},\n", 1828 | " {'MKhumanthem'},\n", 1829 | " {'naebumaye'},\n", 1830 | " {'gakogakotto2'},\n", 1831 | " {'softwarenews42'},\n", 1832 | " {'Lt_Walker9'},\n", 1833 | " {'SE_Barbell'},\n", 1834 | " {'JayEntrust'},\n", 1835 | " {'JobsAshford'},\n", 1836 | " {'MichaelOstuni'},\n", 1837 | " {'w4w3r'},\n", 1838 | " {'AndrzejRama'},\n", 1839 | " {'naderys'},\n", 1840 | " {'nexgespl'},\n", 1841 | " {'husenpb'},\n", 1842 | " {'aepiphanni'},\n", 1843 | " {'KathiawadToday'},\n", 1844 | " {'Ahmed777962'},\n", 1845 | " {'Rate1tter'},\n", 1846 | " {'5th_ghostbuster'},\n", 1847 | " {'TheDotsGroup'},\n", 1848 | " {'FXBAUD'},\n", 1849 | " {'linuxeden_com'},\n", 1850 | " {'DefeatNCD'},\n", 1851 | " {'chaital14315682'},\n", 1852 | " {'_mommarobyn'},\n", 1853 | " {'muliadi_ii'},\n", 1854 | " {'Rishilious22333'},\n", 1855 | " {'phillyinformer'},\n", 1856 | " {'lexxsoft'},\n", 1857 | " {'DruckereiCHBeck'},\n", 1858 | " {'AssamReporter'},\n", 1859 | " {'edmondying'},\n", 1860 | " {'CharBirch92'},\n", 1861 | " {'Cryptonic_Y4n'},\n", 1862 | " {'techcrown03'},\n", 1863 | " {'rasam260'},\n", 1864 | " {'triboland'},\n", 1865 | " {'lowterrain'},\n", 1866 | " {'Rohith62653700'},\n", 1867 | " {'Mpanga96'},\n", 1868 | " {'tee_mars3'},\n", 1869 | " {'oberoi_rohu'},\n", 1870 | " {'Hire_Atl'},\n", 1871 | " {'DragonHasFlown'},\n", 1872 | " {'17lorelei76'},\n", 1873 | " {'kardsen'},\n", 1874 | " {'newsmanofindia'},\n", 1875 | " {'morodog'},\n", 1876 | " {'OracleSql_JP'},\n", 1877 | " {'mitsunoir'},\n", 1878 | " {'worldat247'},\n", 1879 | " {'binetou_gueye'},\n", 1880 | " {'PREcho_de'},\n", 1881 | " {'MrMasudAhmad'},\n", 1882 | " {'AndreasTully'},\n", 1883 | " {'PuneMagazine'},\n", 1884 | " {'pangenomepapers'},\n", 1885 | " {'Jonathonsciola'},\n", 1886 | " {'tracygloverj'},\n", 1887 | " {'vrungta'},\n", 1888 | " {'toshi_tuki'},\n", 1889 | " {'Trialanderror_v'},\n", 1890 | " {'StephanHakan'},\n", 1891 | " {'pankazjosh'},\n", 1892 | " {'risksecure'},\n", 1893 | " {'MAT_Updates'},\n", 1894 | " {'knowlix'},\n", 1895 | " {'dunyawatm'},\n", 1896 | " {'wfrdasilva'},\n", 1897 | " {'DialysisSaves'},\n", 1898 | " {'notimeoff'},\n", 1899 | " {'lingufishcom'},\n", 1900 | " {'NewsFromSPI'},\n", 1901 | " {'CoreyLapka'},\n", 1902 | " {'punga127'},\n", 1903 | " {'nani938'},\n", 1904 | " {'rhetonik'},\n", 1905 | " {'PeterbMangan'},\n", 1906 | " {'gavinpiper108'},\n", 1907 | " {'KumarV61'},\n", 1908 | " {'masaun2551'},\n", 1909 | " {'PANrecruiter'},\n", 1910 | " {'natosepia'},\n", 1911 | " {'drsenri'},\n", 1912 | " {'Corporate88'},\n", 1913 | " {'naixent'},\n", 1914 | " {'CloudGuru6'},\n", 1915 | " {'Amir_Gh97'},\n", 1916 | " {'codehoven'},\n", 1917 | " {'Yan15730195'},\n", 1918 | " {'MacksofyT'},\n", 1919 | " {'WhitepapersOl'},\n", 1920 | " {'tatsuzawa'},\n", 1921 | " {'GoaHeadlines1'},\n", 1922 | " {'RadioUdeG'},\n", 1923 | " {'coco_air'},\n", 1924 | " {'Vaclavp_77'},\n", 1925 | " {'aldaz'},\n", 1926 | " {'ThOneUpKID1'},\n", 1927 | " {'trinity_digest'},\n", 1928 | " {'ViterbiCareers', 'ysoto1'},\n", 1929 | " {'luthermolvera'},\n", 1930 | " {'etoile_cr'},\n", 1931 | " {'HedgeBz', 'pythontic_'},\n", 1932 | " {'chemical_eLii'},\n", 1933 | " {'Fasteners'},\n", 1934 | " {'MOHAMMEDxHASSAN'},\n", 1935 | " {'0Mtbuzzer'},\n", 1936 | " {'Jeannette_Bot'},\n", 1937 | " {'IllinoisRecruit'},\n", 1938 | " {'thiruvananthap1'},\n", 1939 | " {'froshloaded'},\n", 1940 | " {'ITmixCZ'},\n", 1941 | " {'HomeDreamz'},\n", 1942 | " {'kentuckynewsdes', 'lansingnewsnow'},\n", 1943 | " {'jonathanbracam'},\n", 1944 | " {'KathiFajardo'},\n", 1945 | " {'anxiously0307'},\n", 1946 | " {'domainnamesnz'},\n", 1947 | " {'InfoTechnology3'},\n", 1948 | " {'FoundryNewsBot'},\n", 1949 | " {'mtl_ecommerce'},\n", 1950 | " {'chimwemwepaul'},\n", 1951 | " {'arpinstu'},\n", 1952 | " {'YUHKEINCT'},\n", 1953 | " {'eprnetwork'},\n", 1954 | " {'jelly_words'},\n", 1955 | " {'larsen693'},\n", 1956 | " {'cstromblad'},\n", 1957 | " {'Rafael_Asanov'},\n", 1958 | " {'lou_hou'},\n", 1959 | " {'Corey4Progress'},\n", 1960 | " {'APX3D_Printing'},\n", 1961 | " {'project_ltd'},\n", 1962 | " {'moniquenini'},\n", 1963 | " {'Simon_Activist'},\n", 1964 | " {'talk_to_myself7'},\n", 1965 | " {'inspirisys'},\n", 1966 | " {'CryptoLegion'},\n", 1967 | " {'serujio_kmdhe'},\n", 1968 | " {'JobsTemeculaCA'},\n", 1969 | " {'LaughingAoi'},\n", 1970 | " {'menomale_che'},\n", 1971 | " {'nuncapops'},\n", 1972 | " {'ecomputerbooks'},\n", 1973 | " {'AutomationForum'},\n", 1974 | " {'DispurNewsFlash'},\n", 1975 | " {'UBAffiliates'},\n", 1976 | " {'TCMarcum'},\n", 1977 | " {'ZHSLLC'},\n", 1978 | " {'matthewhall78'},\n", 1979 | " {'Cyberjinio'},\n", 1980 | " {'RanchiNewsDesk'},\n", 1981 | " {'gbuckholtz'},\n", 1982 | " {'atom_HeV'},\n", 1983 | " {'EasyPC_in'},\n", 1984 | " {'doserre'},\n", 1985 | " {'AdeolaHadey'},\n", 1986 | " {'GilbertLeconte'},\n", 1987 | " {'GiveInfoMe1'},\n", 1988 | " {'TR_network', 'Toprogrammer'},\n", 1989 | " {'orkin'},\n", 1990 | " {'letisales'},\n", 1991 | " {'TheWebGlobal'},\n", 1992 | " {'OS__Expert'},\n", 1993 | " {'barlocast'},\n", 1994 | " {'remotepeople'},\n", 1995 | " {'DisyDev'},\n", 1996 | " {'HendrixsMoney'},\n", 1997 | " {'Kafka'},\n", 1998 | " {'megha_prn'},\n", 1999 | " {'lovingcatz'},\n", 2000 | " {'KudtarkarParul'},\n", 2001 | " {'fundrais123'},\n", 2002 | " {'brentbisso'},\n", 2003 | " {'MoraAleja55'},\n", 2004 | " {'crmcoach'},\n", 2005 | " {'legnavegroup'},\n", 2006 | " {'MOHIO_GmbH'},\n", 2007 | " {'A10_APU'},\n", 2008 | " {'Trew30_'},\n", 2009 | " {'ambandla'},\n", 2010 | " {'Milner801'},\n", 2011 | " {'barbyware'},\n", 2012 | " {'anyhows_'},\n", 2013 | " {'ItssKansai'},\n", 2014 | " {'DPProfessionals'},\n", 2015 | " {'charles_azar'},\n", 2016 | " {'fininsyn'},\n", 2017 | " {'andryukhin'},\n", 2018 | " {'ChronLaw'},\n", 2019 | " {'SinghadMekha'},\n", 2020 | " {'joemoukarzelone'},\n", 2021 | " {'teamclerk'},\n", 2022 | " {'jenifferiulius'},\n", 2023 | " {'tignear'},\n", 2024 | " {'linksightsio'},\n", 2025 | " {'nighguy'},\n", 2026 | " {'theTrueObserver'},\n", 2027 | " {'TwkIshii'},\n", 2028 | " {'aceouterspace'},\n", 2029 | " {'SkiBuni'},\n", 2030 | " {'echojs2'},\n", 2031 | " {'NiravPatel0204'},\n", 2032 | " {'twjoshua'},\n", 2033 | " {'Browsify'},\n", 2034 | " {'NicoleCheaven'},\n", 2035 | " {'WardCorbett'},\n", 2036 | " {'KhraisNoor'},\n", 2037 | " {'elitorr61842801'},\n", 2038 | " {'m_afatah'},\n", 2039 | " {'MyAlliesNews'},\n", 2040 | " {'SofiaITC'},\n", 2041 | " {'swankylynx'},\n", 2042 | " {'khalkeus3d'},\n", 2043 | " {'DigitalDownloa4'},\n", 2044 | " {'imoff333'},\n", 2045 | " {'Hshmear84'},\n", 2046 | " {'mikereys_sag'},\n", 2047 | " {'karaszewicz'},\n", 2048 | " {'MixMax123456'},\n", 2049 | " {'StackBounty'},\n", 2050 | " {'mrclydecarty'},\n", 2051 | " {'DelhiToday1'},\n", 2052 | " {'node_program'},\n", 2053 | " {'collegeprozheh'},\n", 2054 | " {'wakajobs'},\n", 2055 | " {'harbingertimes'},\n", 2056 | " {'DmitryShaman'},\n", 2057 | " {'Patachoup'},\n", 2058 | " {'NanyBKK'},\n", 2059 | " {'CounselUpdates'},\n", 2060 | " {'zkalvi'},\n", 2061 | " {'tribes_ai'},\n", 2062 | " {'daryna_shu'},\n", 2063 | " {'analysees'},\n", 2064 | " {'Making_Science_'},\n", 2065 | " {'BigData_TT'},\n", 2066 | " {'ourbennie'},\n", 2067 | " {'AlexPorumbel'},\n", 2068 | " {'MuawiaTechno'},\n", 2069 | " {'webready_se'},\n", 2070 | " {'h2onolan'},\n", 2071 | " {'doctorSturza'},\n", 2072 | " {'Fujitsu_FJCT'},\n", 2073 | " {'jperezdelolmo'},\n", 2074 | " {'1001tweetstest'},\n", 2075 | " {'benish369'},\n", 2076 | " {'Tibo_st'},\n", 2077 | " {'grepdev'},\n", 2078 | " {'SpicaTerrible_'},\n", 2079 | " {'yaxye2006'},\n", 2080 | " {'mpdrsn'},\n", 2081 | " {'D0m1s0l'},\n", 2082 | " {'tammyvwyatt'},\n", 2083 | " {'janeurby'},\n", 2084 | " {'ErieMom'},\n", 2085 | " {'JammuJournal'},\n", 2086 | " {'robertopliegor'},\n", 2087 | " {'serfusE_'},\n", 2088 | " {'JamshedpurRepor'},\n", 2089 | " {'NCROnlineNews'},\n", 2090 | " {'jjonisius'},\n", 2091 | " {'ForestWiki', 'PythonLinks'},\n", 2092 | " {'ictjob_1st_job'},\n", 2093 | " {'Madhuri01715'},\n", 2094 | " {'NagpurNewsDesk'},\n", 2095 | " {'HNTopStories'},\n", 2096 | " {'andreaslindner'},\n", 2097 | " {'marketemia'},\n", 2098 | " {'GasMarketing5'},\n", 2099 | " {'AhamPiyush'},\n", 2100 | " {'KeralaDaily1'},\n", 2101 | " {'MIRresearch'},\n", 2102 | " {'amico_nick'},\n", 2103 | " {'TheAndroid2011'},\n", 2104 | " {'davy_duboy'},\n", 2105 | " {'hoconinfo'},\n", 2106 | " {'JWeee'},\n", 2107 | " {'WFranceL'},\n", 2108 | " {'BorkowskMarcin'},\n", 2109 | " {'myshopzeemart'},\n", 2110 | " {'LPBoyer09'},\n", 2111 | " {'PDFviewerForWp'},\n", 2112 | " {'vikatakavi11'},\n", 2113 | " {'Rvgautam40RAVI'},\n", 2114 | " {'travailbelgique'},\n", 2115 | " {'estranhow', 'leonardodna'},\n", 2116 | " {'Tripura_News'},\n", 2117 | " {'technewspr'},\n", 2118 | " {'GadgetJoseph'},\n", 2119 | " {'unlikeanything'},\n", 2120 | " {'GustiiMCBA'},\n", 2121 | " {'BhubaneswarNew1'},\n", 2122 | " {'yoshieda_mn'},\n", 2123 | " {'ben_thijssen'},\n", 2124 | " {'brokoh1'},\n", 2125 | " {'nesoxy'},\n", 2126 | " {'HalmagyiCsaba'},\n", 2127 | " {'SnehaCh93683369'},\n", 2128 | " {'Avtora', 'social_pipe'},\n", 2129 | " {'SophisticatedCT'},\n", 2130 | " {'_nove_nove_'},\n", 2131 | " {'DeborahRoszell'},\n", 2132 | " {'OnlinePrNew'},\n", 2133 | " {'TechKnights_UCF'},\n", 2134 | " {'atoast2toast'},\n", 2135 | " {'tupples'},\n", 2136 | " {'CloudTenIT'},\n", 2137 | " {'motivasyonbuk'},\n", 2138 | " {'Viropera'},\n", 2139 | " {'techieappy'},\n", 2140 | " {'NHMeetings'},\n", 2141 | " {'itotto0205'},\n", 2142 | " {'b4dcode'},\n", 2143 | " {'Lingam40670559'},\n", 2144 | " {'Ahmadanii2'},\n", 2145 | " {'rsoftsolution'},\n", 2146 | " {'mint398'},\n", 2147 | " {'TheSmartIT'},\n", 2148 | " {'StoreConsulting'},\n", 2149 | " {'TheWiseFool4'},\n", 2150 | " {'acilnumara_uk'},\n", 2151 | " {'Paddy_Owens66'},\n", 2152 | " {'Pyenews2'},\n", 2153 | " {'JobsLowellMA'},\n", 2154 | " {'isabela2599'},\n", 2155 | " {'Sonya__Briggs'},\n", 2156 | " {'deeptodive', 'illusiontec'},\n", 2157 | " {'asapdiablo1'},\n", 2158 | " {'anderso62095558'},\n", 2159 | " {'4MrKW'},\n", 2160 | " {'leonardonam'},\n", 2161 | " {'mparkerCSI'},\n", 2162 | " {'TechPlot'},\n", 2163 | " {'AndhraPradeshJo'},\n", 2164 | " {'trevars'},\n", 2165 | " {'netmobz'},\n", 2166 | " {'HyperedgeEmbed'},\n", 2167 | " {'Ohionewsdesk', 'Oklahomacityhea'},\n", 2168 | " {'san_jose__jobs'},\n", 2169 | " {'acronymlister'},\n", 2170 | " {'KrzysiekRichter'},\n", 2171 | " {'tinafanson1124'},\n", 2172 | " {'XmarketReports'},\n", 2173 | " {'RaipurDaily'},\n", 2174 | " {'tennis6399'},\n", 2175 | " {'oss_sh'},\n", 2176 | " {'Cleora_Kramer'},\n", 2177 | " {'TechNewsJunkies'},\n", 2178 | " {'AphnoMarketing'},\n", 2179 | " {'jhleebitninene1'},\n", 2180 | " {'BasslKoukash'},\n", 2181 | " {'MonkOnDMT'},\n", 2182 | " {'suduo1233'},\n", 2183 | " {'OneBaldMan'},\n", 2184 | " {'Ntou4', 'tetoran6'},\n", 2185 | " {'AnaLaur15266130'},\n", 2186 | " {'Reisemate'},\n", 2187 | " {'stepik_reviews'},\n", 2188 | " {'BundelkhandOJ'},\n", 2189 | " {'BangaloreSamach'},\n", 2190 | " {'AmosTrack'},\n", 2191 | " {'ucl_discovery'},\n", 2192 | " {'noris_dev'},\n", 2193 | " {'odia_reports'},\n", 2194 | " {'Jai_Cilento'},\n", 2195 | " {'reachgilly'},\n", 2196 | " {'kawaguchi_com'},\n", 2197 | " {'besttechtrade'},\n", 2198 | " {'viqi_efendi'},\n", 2199 | " {'DreslerKlarissa'},\n", 2200 | " {'Photoshop_4u_'},\n", 2201 | " {'JAX_TechEvents'},\n", 2202 | " {'IlyaKomendantov'},\n", 2203 | " {'ruanchaves93'},\n", 2204 | " {'BlakeTrinityQu1'},\n", 2205 | " {'lyrixx_rss'},\n", 2206 | " {'TaxActPro'},\n", 2207 | " {'callennartsson'},\n", 2208 | " {'nurfawaiq'},\n", 2209 | " {'HaysDigiJobsUKI'},\n", 2210 | " {'parvezkhusro'},\n", 2211 | " {'RichardThoming'},\n", 2212 | " {'GazonArtificiel'},\n", 2213 | " {'storminwong'},\n", 2214 | " {'ashwinisundar', 'uftjob'},\n", 2215 | " {'Hiring_Atl'},\n", 2216 | " {'supercilious_fa'},\n", 2217 | " {'soladime_piece'},\n", 2218 | " {'entrepreneur_cm'},\n", 2219 | " {'talhatarik'},\n", 2220 | " {'jiayoulixu'},\n", 2221 | " {'LemonAnt'},\n", 2222 | " {'NMEtoday'},\n", 2223 | " {'PhilSallaway'},\n", 2224 | " {'claire83277378'},\n", 2225 | " {'123trabajo'},\n", 2226 | " {'VadodaraNews'},\n", 2227 | " {'thebyrdlab'},\n", 2228 | " {'objectofpower'},\n", 2229 | " {'IndoreOnlineJou'},\n", 2230 | " {'MarathaHeadline'},\n", 2231 | " {'MonjuSarder'},\n", 2232 | " {'continentspirit'},\n", 2233 | " {'XTechNewsV2'},\n", 2234 | " {'clr_develop'},\n", 2235 | " {'BusinessTrumpet'},\n", 2236 | " {'HaridwarToday'},\n", 2237 | " {'KSD_research'},\n", 2238 | " {'jhu_ai'},\n", 2239 | " {'prdpXbot'},\n", 2240 | " {'daviddryannn'},\n", 2241 | " {'SOMESH505'},\n", 2242 | " {'usamaofkarachi'},\n", 2243 | " {'IndiaNewsMagazi'},\n", 2244 | " {'WisdomjobsS'},\n", 2245 | " {'adster85'},\n", 2246 | " {'caquele_'},\n", 2247 | " {'AlexOpsAI'},\n", 2248 | " {'spstrasser'},\n", 2249 | " {'thephillirodney'},\n", 2250 | " {'dekdclubja'},\n", 2251 | " {'RighteousCrone'},\n", 2252 | " {'randomhub_'},\n", 2253 | " {'innocentinforma'},\n", 2254 | " {'GHasselwander'},\n", 2255 | " {'enrique_larriba'},\n", 2256 | " {'xjzhou'},\n", 2257 | " {'KohimaNewsPaper'},\n", 2258 | " {'recinet'},\n", 2259 | " {'AsaShinonome'},\n", 2260 | " {'Geeksarefunny'},\n", 2261 | " {'build_trumpwall'},\n", 2262 | " {'MurrietaCAJobs'},\n", 2263 | " {'mukundkmishra'},\n", 2264 | " {'Infoshoc'},\n", 2265 | " {'sahdevt'},\n", 2266 | " {'Ric9871Ric'},\n", 2267 | " {'Jesse_V_Burke'},\n", 2268 | " {'AmbalaHerald'},\n", 2269 | " {'ZoricaBel'},\n", 2270 | " {'uijft'},\n", 2271 | " {'Fumon'},\n", 2272 | " {'themuradonian'},\n", 2273 | " {'aka_BlackBadger'},\n", 2274 | " {'Mark20004DC'},\n", 2275 | " {'takerui'},\n", 2276 | " {'RhiscoGroup'},\n", 2277 | " {'HankinsSusanna'},\n", 2278 | " {'CheekDots'},\n", 2279 | " {'edyrmonroym'},\n", 2280 | " {'Jdking1920'},\n", 2281 | " {'ExactOptionPick'},\n", 2282 | " {'KoutsounisV'},\n", 2283 | " {'theresa_ehlen'},\n", 2284 | " {'BhagalpurToday1'},\n", 2285 | " {'Shulab'},\n", 2286 | " {'RadhikaKaruturi'},\n", 2287 | " {'stefanialevanti'},\n", 2288 | " {'jskim007961'},\n", 2289 | " {'Camille_OS'},\n", 2290 | " {'Malvatronics'},\n", 2291 | " {'EagleStarNET'},\n", 2292 | " {'KallelSaoussan'},\n", 2293 | " {'BESTWarszawa'},\n", 2294 | " {'bambillio'},\n", 2295 | " {'SimiPam_'},\n", 2296 | " {'victorjuliord'},\n", 2297 | " {'Techset4'},\n", 2298 | " {'ShariBermanATL'},\n", 2299 | " {'wicem'},\n", 2300 | " {'ArashRahimian'},\n", 2301 | " {'ahotdiscount'},\n", 2302 | " {'kodkneg'},\n", 2303 | " {'kumar70011'},\n", 2304 | " {'digitallifest10'},\n", 2305 | " {'FlatL1ne'},\n", 2306 | " {'LaravelPackages'},\n", 2307 | " {'DiabetesShare'},\n", 2308 | " {'PanipatHeadline'},\n", 2309 | " {'butchclark5'},\n", 2310 | " {'YassinBIBI5'},\n", 2311 | " {'AsagiriDesign'},\n", 2312 | " {'MNoorFawi'},\n", 2313 | " {'fsdqui'},\n", 2314 | " {'rhcp1010'},\n", 2315 | " {'LochaberLocal'},\n", 2316 | " {'kabochallah'},\n", 2317 | " {'zennie_fic'},\n", 2318 | " {'antony_wijay'},\n", 2319 | " {'1TheBlessed'},\n", 2320 | " {'anshu_kandhari'},\n", 2321 | " {'mdjubair_me'},\n", 2322 | " {'CryptoDaniella'},\n", 2323 | " {'fusercan'},\n", 2324 | " {'simulevski'},\n", 2325 | " {'natamvo'},\n", 2326 | " {'sajjadkazemi10'},\n", 2327 | " {'TheChestnutPost'},\n", 2328 | " {'fitditcorps'},\n", 2329 | " {'GetDataIO'},\n", 2330 | " {'boundatacamp'},\n", 2331 | " {'teknoteriyak'},\n", 2332 | " {'viqdy'},\n", 2333 | " {'AstrorEnales'},\n", 2334 | " {'LucasCollege'},\n", 2335 | " {'zientekglobal'},\n", 2336 | " {'WallyWave'},\n", 2337 | " {'KolkataNToday'},\n", 2338 | " {'aasisvinayak'},\n", 2339 | " {'Mrr_Zo'},\n", 2340 | " {'MaharashtraHera'},\n", 2341 | " {'Azim_Palmer'},\n", 2342 | " {'1clickdeploy'}]" 2343 | ] 2344 | }, 2345 | "execution_count": 81, 2346 | "metadata": {}, 2347 | "output_type": "execute_result" 2348 | } 2349 | ], 2350 | "source": [ 2351 | "list(nx.community.label_propagation_communities(G))" 2352 | ] 2353 | } 2354 | ], 2355 | "metadata": { 2356 | "kernelspec": { 2357 | "display_name": "Python 3", 2358 | "language": "python", 2359 | "name": "python3" 2360 | }, 2361 | "language_info": { 2362 | "codemirror_mode": { 2363 | "name": "ipython", 2364 | "version": 3 2365 | }, 2366 | "file_extension": ".py", 2367 | "mimetype": "text/x-python", 2368 | "name": "python", 2369 | "nbconvert_exporter": "python", 2370 | "pygments_lexer": "ipython3", 2371 | "version": "3.7.6" 2372 | } 2373 | }, 2374 | "nbformat": 4, 2375 | "nbformat_minor": 4 2376 | } 2377 | --------------------------------------------------------------------------------