├── .gitignore ├── LICENSE ├── README.md ├── data └── .gitkeep ├── paper ├── paper.bib └── paper.md ├── random_graph_generator ├── __init__.py ├── __main__.py ├── conf_example.json ├── dynamic_random_graph.py └── fast_dynamic_random_graph.py ├── requirements.txt └── tests ├── __init__.py ├── conftest.py ├── test_fast_random_graph.py ├── test_networkx.py └── utils.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Specific 2 | *.csv 3 | *.edge_list 4 | *.snapshot 5 | .vscode/ 6 | 7 | # Byte-compiled / optimized / DLL files 8 | __pycache__/ 9 | *.py[cod] 10 | *$py.class 11 | 12 | # C extensions 13 | *.so 14 | 15 | # Distribution / packaging 16 | .Python 17 | build/ 18 | develop-eggs/ 19 | dist/ 20 | downloads/ 21 | eggs/ 22 | .eggs/ 23 | lib/ 24 | lib64/ 25 | parts/ 26 | sdist/ 27 | var/ 28 | wheels/ 29 | share/python-wheels/ 30 | *.egg-info/ 31 | .installed.cfg 32 | *.egg 33 | MANIFEST 34 | 35 | # PyInstaller 36 | # Usually these files are written by a python script from a template 37 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 38 | *.manifest 39 | *.spec 40 | 41 | # Installer logs 42 | pip-log.txt 43 | pip-delete-this-directory.txt 44 | 45 | # Unit test / coverage reports 46 | htmlcov/ 47 | .tox/ 48 | .nox/ 49 | .coverage 50 | .coverage.* 51 | .cache 52 | nosetests.xml 53 | coverage.xml 54 | *.cover 55 | *.py,cover 56 | .hypothesis/ 57 | .pytest_cache/ 58 | cover/ 59 | 60 | # Translations 61 | *.mo 62 | *.pot 63 | 64 | # Django stuff: 65 | *.log 66 | local_settings.py 67 | db.sqlite3 68 | db.sqlite3-journal 69 | 70 | # Flask stuff: 71 | instance/ 72 | .webassets-cache 73 | 74 | # Scrapy stuff: 75 | .scrapy 76 | 77 | # Sphinx documentation 78 | docs/_build/ 79 | 80 | # PyBuilder 81 | .pybuilder/ 82 | target/ 83 | 84 | # Jupyter Notebook 85 | .ipynb_checkpoints 86 | 87 | # IPython 88 | profile_default/ 89 | ipython_config.py 90 | 91 | # pyenv 92 | # For a library or package, you might want to ignore these files since the code is 93 | # intended to run in multiple environments; otherwise, check them in: 94 | # .python-version 95 | 96 | # pipenv 97 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 98 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 99 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 100 | # install all needed dependencies. 101 | #Pipfile.lock 102 | 103 | *.prof 104 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 105 | __pypackages__/ 106 | 107 | # Celery stuff 108 | celerybeat-schedule 109 | celerybeat.pid 110 | 111 | # SageMath parsed files 112 | *.sage.py 113 | 114 | # Environments 115 | .env 116 | .venv 117 | env/ 118 | venv/ 119 | ENV/ 120 | env.bak/ 121 | venv.bak/ 122 | 123 | # Spyder project settings 124 | .spyderproject 125 | .spyproject 126 | 127 | # Rope project settings 128 | .ropeproject 129 | 130 | # mkdocs documentation 131 | /site 132 | 133 | # mypy 134 | .mypy_cache/ 135 | .dmypy.json 136 | dmypy.json 137 | 138 | # Pyre type checker 139 | .pyre/ 140 | 141 | # pytype static type analyzer 142 | .pytype/ 143 | 144 | # Cython debug symbols 145 | cython_debug/ -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | # DynamicRandomGraphs: A Python package for generation of scalable temporal random graphs 3 | [![No Maintenance Intended](http://unmaintained.tech/badge.svg)](http://unmaintained.tech/) 4 | 5 | 6 | 7 | ## About The Project 8 | 9 | DynamicRandomGraphs provides implementations of dynamic networks in Python. It deals with the efficient generation of 10 | scalable dynamic (temporal) random networks. 11 | 12 | The package was first used to simulate the spread of Covid-19 under different social distancing conditions as presented 13 | in Networks 2021 and in Arxiv paper titled: An interaction-based contagion model over temporal networks demonstrates that reducing temporal network density reduces total infection rate. 14 | When using the package please cite the Arxiv paper: 15 | Abbey, A., Marmor, Y., Shahar, Y., Mokryn, O. (2022). An interaction-based contagion model over temporal networks demonstrates that reducing temporal network density reduces total infection rate. ArXiv 2202.11591, 1–21. https://arxiv.org/abs/2202.11591 16 | 17 | The talk at Networks2021: [Reducing temporal density reduces total infection rate](https://www.youtube.com/watch?v=gUyP7etPPvE) 18 | 19 | 20 | 21 | # Getting started 22 | 23 | ## Prerequisites 24 | 25 | Make sure you have `Python3` installed on your computer, as well as `pip` and `setuptools`, and that they are up to 26 | date. If you want to be safe, take a look at 27 | the [guide to installing Python packages](https://packaging.python.org/tutorials/installing-packages/). 28 | 29 | Clone the codebase from this GitHub repository with the small green icon on the up-right corner `Code` or from the 30 | terminal: 31 | 32 | ```shell 33 | git clone ScanLab-ossi/random_dynamic_graph 34 | ``` 35 | 36 | Then, install the required packages: 37 | 38 | ```sh 39 | pip install -r requirements.txt 40 | ``` 41 | 42 | ## Usage 43 | 44 | Our package operates in two modes. A stand-alone mode allows for the generation of a series of graphs representing the 45 | temporal network of choice; The parallel mode creates a set of temporal networks, each consisting of multiple graphs, 46 | according to a predefined set of parameters. 47 | 48 | ### Stand-alone mode 49 | 50 | ```shell 51 | python -m random_graph_generator --node=100 --steps=200 --mode="pandas" 52 | ``` 53 | 54 | This command will create network with 100 nodes along 200 time steps. That information will be saved as CSV file. 55 | 56 | Similar example will create NetworkX object, and the relevant command is: 57 | 58 | ```shell 59 | python -m random_graph_generator --node=100 --steps=200 --mode="NetworkX" 60 | ``` 61 | 62 | Other parameters are accessible for that mode: 63 | 64 | ``` 65 | Parameters 66 | ---------- 67 | n : int 68 | The number of nodes. 69 | steps: int 70 | The number of timestamps in the temporal network. The length of the network in the time axis. 71 | up_rate : float 72 | Probability for edge creation. 73 | down_rate: float 74 | Probability of edge removal 75 | write_to_csv: bool, optional (default=False) 76 | False will return pandas dataframe, True will write the results to CSV (preferred for huge networks) 77 | output_file_name : str 78 | Destination for csv file, relevant only if ''write_to_csv'' is True 79 | is_directed : bool, optional (default=False) 80 | If True, this function returns a directed graph. 81 | ``` 82 | 83 | ### Parallel mode 84 | 85 | Many networks can be created in parallel in this mode of operation. To do so, use the JSON file as the ` 86 | conf_example.json``. 87 | 88 | ```shell 89 | python -m random_graph_generator --config="conf_example.json" 90 | ``` 91 | 92 | The json example looks like: 93 | 94 | ```json 95 | { 96 | "nodes": [ 97 | 100, 98 | 200, 99 | 500 100 | ], 101 | "steps": [ 102 | 1000, 103 | 2000 104 | ], 105 | "up": [ 106 | 0.001, 107 | 0.005 108 | ], 109 | "down": [ 110 | 0.2, 111 | 0.4 112 | ], 113 | "is_directed": [ 114 | false 115 | ] 116 | } 117 | ``` 118 | 119 | The generator makes a graph from every combination of parameters in the lists. From the example above, the generator 120 | creates 24 different networks. 121 | 122 | # License 123 | 124 | Distributed under the MIT License. See `LICENSE` for more information. 125 | 126 | Cite: 127 | Abbey, A., Marmor, Y., Shahar, Y.,Mokryn, O. (2022). An interaction-based contagion model over temporal networks demonstrates that reducing temporal network density reduces total infection rate. ArXiv 2202.11591, 1–21. https://arxiv.org/abs/2202.11591 128 | 129 | # Roadmap 130 | 131 | See the [open issues](https://github.com/ScanLab-ossi/random_dynamic_graph/issues) for a list of proposed features (and 132 | known issues). 133 | 134 | In general, we would like to extend the model for better control on the parameters on the time-line and on the degrees 135 | distribution and communities parameters as well. 136 | 137 | ## Contributing 138 | 139 | Feel free to fork and help out. 140 | 141 | Test using `pytest`. Run slower tests with `pytest --runslow`. 142 | 143 | ## Related works 144 | 145 | * [NetworkX](https://networkx.org/) - a Python package for the creation, manipulation, and study of the structure, 146 | dynamics, and functions of complex networks (Great package, but not suitable for large networks or temporal networks). 147 | * [DyNetx](https://dynetx.readthedocs.io/en/latest/index.html) - a Python software package that extends NetworkX with 148 | dynamic network models and algorithms (Excellent package, but performance in large-scale temporal networks is limited) 149 | . 150 | * [DANCer](https://perso.univ-st-etienne.fr/largeron/DANCer_Generator/#reference) - a Scala software that generates 151 | dynamic attributed networks with community structure that follow the known properties of real-world 152 | networks ([paper](https://hal-auf.archives-ouvertes.fr/hal-01377321/document)). 153 | * [pathpy](https://www.pathpy.net/) - an Open Source python package providing higher-order network analytics for time 154 | series data. 155 | * [TACOMA](https://github.com/benmaier/tacoma) - TemporAl COntact Modeling and Analysis. Provides fast tools to analyze 156 | temporal contact networks, produce surrogate networks using qualitative models and simulate Gillespie processes on 157 | them. TACOMA is a joint C++/Python-package for the modeling and analysis of undirected and unweighted temporal 158 | networks, with a focus on (but not limited to) human face-to-face contact networks. 159 | * [Teneto](https://teneto.readthedocs.io/en/latest/index.html) - a Python package includes various tools for analyzing 160 | temporal network data. Temporal network measures, temporal network generation, derivation of time-varying/dynamic 161 | connectivity, plotting functions. 162 | * [netrd](https://netrd.readthedocs.io/en/latest/) - This library provides a consistent, NetworkX-based interface to 163 | various utilities for graph distances, graph reconstruction from time series data, and simulated dynamics on networks. 164 | * [Random Modular Network Generator](https://github.com/prathasah/random-modular-network-generator) Generates random 165 | graphs with tunable strength of community structure 166 | * [randomGraph](https://github.com/sdghafouri/randomGraph) very simple random graph generator in matlab 167 | * [Graph1](https://github.com/Saptaparni/Graph1) Random Graph Generator with Max capacity paths (C++) 168 | * [grapherator](https://github.com/jakobbossek/grapherator) - The R package grapherator implements a modular approach to 169 | benchmark graph generation focusing on undirected, weighted graphs. 170 | * [igraph: Network Analysis and Visualization](https://cran.r-project.org/package=igraph) This R package Includes some 171 | methods to generate classical Erdos-Renyi random graphs as well as more recent models, e.g., small-world graphs. 172 | * [netgen: Network Generator for Combinatorial Graph Problems](https://cran.r-project.org/package=netgen) This R package 173 | Contains some methods to generate complete graphs especially for benchmarking Travelling-Salesperson-Problem solvers. 174 | * [bnlearn: Bayesian Network Structure Learning, Parameter Learning and Inference](https://cran.r-project.org/web/packages/bnlearn/index.html) 175 | The R function `bnlearn::random.graph` implements some algorithms to create graphs. 176 | 177 | ## How to report issues? 178 | 179 | Running into any bugs? Check out the [open issues](https://github.com/ScanLab-ossi/random_dynamic_graph/issues) to see 180 | if we're already working on it. If not, open up a new issue, and we will check it out when we can. Thank you. 181 | 182 | 183 | 184 | -------------------------------------------------------------------------------- /data/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ScanLab-ossi/DynamicRandomGraphs/31ae70aeff7f2d4325cd3b43efeeb9cc9a299667/data/.gitkeep -------------------------------------------------------------------------------- /paper/paper.bib: -------------------------------------------------------------------------------- 1 | @article{Zhang:2017, 2 | title={Random graph models for dynamic networks}, 3 | author={Zhang, Xiao and Moore, Cristopher and Newman, Mark EJ}, 4 | journal={The European Physical Journal B}, 5 | volume={90}, 6 | number={10}, 7 | pages={1--14}, 8 | year={2017}, 9 | publisher={Springer} 10 | } -------------------------------------------------------------------------------- /paper/paper.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: 'DynamicRandomGraphs: A Python package for generation of scalable temporal random graphs' 3 | 4 | tags: 5 | 6 | - Python 7 | - Network Science 8 | - Temporal Networks 9 | - Random Graphs 10 | 11 | authors: 12 | 13 | - name: Yanir Marmor affiliation: 1 14 | - name: Alex Z. Abbey affiliation: 1 15 | - name: Osnat Mokryn affiliation: 1 16 | affiliations: 17 | - name: University of Haifa index: 1 18 | 19 | date: 27 June 2021 bibliography: paper.bib link-citations: yes 20 | 21 | # Summary 22 | 23 | Large-scale real-world interaction systems, such as social, technological, and biological networks, are dynamic 24 | structures that change with time. There is an increased interest in studying the dynamics and temporal evolution of 25 | these systems. One of the ways is by modeling these systems using dynamic temporal networks. 26 | 27 | Models for studying networks are primarily static. Lately, the work in `[@Zhang:2017]` offered natural generalization 28 | of the Erdős–Rényi static network model, where one assumes that continuous-time Markov processes govern the 29 | appearance and disappearance of edges. Thus, the fundamental unit of analysis is the entire history of the network. 30 | Edges appear and disappear by making transitions from present to absent or vice versa at certain rates. For example, in 31 | temporal random networks, the rate depends on the required probability of having an edge between any two nodes 32 | (vertices). Such temporal modeling is also significant for the research and understanding of virality and epidemics: 33 | airborne diseases spread over networks of contacts between individuals that change in time, and ideas dynamically spread 34 | over social networks. 35 | 36 | `RandomDynamicGraph` is a Python package that implements the algorithm from `[@Zhang:2017]` for generating large-scale 37 | dynamic random graphs. The package focuses on massive data generation; it uses efficient math calculations, writes to 38 | file instead of in-memory when datasets are too large, and it supports multi-processing. 39 | 40 | `DynamicRandomGraphs` was created for use by network scientists and other researchers who use temporal networks to 41 | represent their data, as well as students in courses on dynamic networks, complex networks, and networks science. The 42 | package was first used to simulate the spread of Covid-19 under different social distancing conditions as presented in 43 | Networks 2021: [Reducing temporal density reduces total infection rate](https://www.youtube.com/watch?v=gUyP7etPPvE). 44 | 45 | # Statement of need 46 | 47 | Lack of real-life temporal network data due to data collection cost, user privacy, and up-to-date and relevant 48 | information issues creates a need for temporal network models. Current temporal network models tend to be either static 49 | or of small size. 50 | 51 | This library permits in-depth theoretical and empirical analysis of large-scale temporal networks; it does so without 52 | requiring a large investment in data collecting and without jeopardizing human privacy. 53 | 54 | # Other packages 55 | 56 | A popular software package is `NetworkX`. NetworkX is a great package for creating, manipulating, and studying complex 57 | networks' structure, dynamics, and functions but is unsuitable for large temporal networks. 58 | `DyNetx` is an extension of `NetworkX` for dynamic graphs. Unfortunately, Because of the dependence on NetworkX, 59 | `DyNetx`'s performance is deficient in large-scale temporal networks. 60 | `netrd` is another Python library based on the `NetworkX` interface and provides utilities for dynamic graphs. In the 61 | context of random graph generation, there are some libraries in non-Python languages: `DANCer` (Scala), `randomGraph` ( 62 | Matlab), `Graph1`(C++), `grapherator`(R), `igraph`(R) etc. 63 | 64 | In contrast to the other mentioned packages,`DynamicRandomGraphs`, suggested here, is a Python package associated with 65 | dynamic networks that generate random temporal networks`[@Zhang:2017]`. Python allows the wrapping of low-level 66 | languages for speed without sacrificing flexibility or user-friendliness in the user interface. 67 | The `DynamicRandomGraphs` API was created to provide a class-based and user-friendly interface for generating 68 | large-scale random dynamic networks efficiently. Conversion of `DynamicRandomGraphs` objects to `NetworkX` objects is 69 | supported but inefficient. 70 | 71 | # Support 72 | 73 | Bug reports and feature requests are highly appreciated via the GitHub issue tracker. 74 | 75 | # References -------------------------------------------------------------------------------- /random_graph_generator/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ScanLab-ossi/DynamicRandomGraphs/31ae70aeff7f2d4325cd3b43efeeb9cc9a299667/random_graph_generator/__init__.py -------------------------------------------------------------------------------- /random_graph_generator/__main__.py: -------------------------------------------------------------------------------- 1 | import itertools 2 | import multiprocessing 3 | import json 4 | from joblib import Parallel, delayed 5 | from pathlib import Path 6 | 7 | import random_graph_generator.fast_dynamic_random_graph as fdrg 8 | import random_graph_generator.dynamic_random_graph as drg 9 | 10 | import argparse 11 | 12 | parser = argparse.ArgumentParser(description="Random Network generation") 13 | 14 | parser.add_argument("--out_file", help="path to output network file") 15 | parser.add_argument("--nodes", default=100, type=int, help="Number of nodes") 16 | parser.add_argument("--steps", default=1000, type=int, help="Number of time steps") 17 | parser.add_argument( 18 | "--is_directed", default=False, type=bool, help="is directed graph (default=False)" 19 | ) 20 | parser.add_argument( 21 | "--up", 22 | default=0.01, 23 | type=float, 24 | help="up-rate parameter; the probability of an edge to appear at the first time (default=0.01)", 25 | ) 26 | parser.add_argument( 27 | "--down", 28 | default=0.5, 29 | type=float, 30 | help="down-rate parameter;" 31 | " the probability of an exist edge to disappear at the next time (default=0.5)", 32 | ) 33 | parser.add_argument( 34 | "--mode", 35 | default="pandas", 36 | type=str, 37 | help="Mode of the network - pandas-based or networkX (default=pandas)", 38 | ) 39 | parser.add_argument( 40 | "--config", 41 | default=None, 42 | type=str, 43 | help="path of config json file for several networks parameters", 44 | ) 45 | 46 | if __name__ == "__main__": 47 | args = parser.parse_args() 48 | 49 | if args.config: 50 | # in case of configuration file, we apply multi-processing generation of any combination of given parameters. 51 | # works only for fast graph generator (pandas-mode) 52 | with open(Path("random_graph_generator") / args.config) as json_file: 53 | args = json.load(json_file) 54 | num_cores = multiprocessing.cpu_count() 55 | parameters_combinations = itertools.product( 56 | args["nodes"], 57 | args["steps"], 58 | args["up"], 59 | args["down"], 60 | args["is_directed"], 61 | ) 62 | results = Parallel(n_jobs=num_cores)( 63 | delayed(fdrg.fast_dynamic_er_random_graph)(*i) 64 | for i in parameters_combinations 65 | ) 66 | else: 67 | if args.mode == "pandas": 68 | fdrg.fast_dynamic_er_random_graph( 69 | n=args.nodes, 70 | steps=args.steps, 71 | up_rate=args.up, 72 | down_rate=args.down, 73 | write_to_csv=True, 74 | is_directed=args.is_directed, 75 | output_file_name=args.out_file, 76 | ) 77 | else: # NetworkX mode 78 | drg.dynamic_er_random_graph( 79 | n=args.nodes, 80 | steps=args.steps, 81 | up_rate=args.up, 82 | down_rate=args.down, 83 | is_directed=args.is_directed, 84 | write_to_file=True, 85 | output_file_name=args.out_file, 86 | ) 87 | -------------------------------------------------------------------------------- /random_graph_generator/conf_example.json: -------------------------------------------------------------------------------- 1 | { 2 | "nodes": [100, 200, 500], 3 | "steps": [1000, 2000], 4 | "up": [0.001, 0.005], 5 | "down": [0.2, 0.4], 6 | "is_directed": [false] 7 | } -------------------------------------------------------------------------------- /random_graph_generator/dynamic_random_graph.py: -------------------------------------------------------------------------------- 1 | import itertools 2 | from random import random 3 | 4 | import dynetx as dn 5 | import networkx as nx 6 | 7 | 8 | def get_edges_iterator(n: int, directed=False): 9 | if directed: 10 | edges = itertools.permutations(range(n), 2) 11 | else: 12 | edges = itertools.combinations(range(n), 2) 13 | return edges 14 | 15 | 16 | def dynamic_er_random_graph( 17 | n, 18 | steps, 19 | up_rate, 20 | down_rate, 21 | is_directed=False, 22 | write_to_file=False, 23 | output_file_name="random_network", 24 | ): 25 | """Returns a $G_{n,mu, lambda}$ dynamic random graph, also known as an dynamic Erdős-Rényi graph. 26 | The $G_{n,\mu, \lambda}$ model chooses to create inexist edges with probabilty $\mu$ (also known as up-rate) 27 | and remove exist edges with probability $\lambda$ (also known as down-rate). 28 | Parameters 29 | ---------- 30 | output_file_name 31 | write_to_file 32 | n : int 33 | The number of nodes. 34 | steps: int 35 | The number of timestamps in the temporal network. The length of the network in the time axis. 36 | up_rate : float 37 | Probability for edge creation. 38 | down_rate: float 39 | Probability of edge removal 40 | seed : integer, random_state, or None (default) 41 | Indicator of random number generation state. 42 | See :ref:`Randomness`. 43 | is_directed : bool, optional (default=False) 44 | If True, this function returns a directed graph. 45 | See Also 46 | -------- 47 | gnp_random_graph 48 | fast_gnp_random_graph 49 | 50 | Notes 51 | ----- 52 | This algorithm runs in $O(?)$ time. 53 | 54 | References 55 | ---------- 56 | .. [1] Mandjes, M., Starreveld, N., Bekker, R., & Spreij, P. (2019). Dynamic Erdős-Rényi Graphs. 57 | In Computing and Software Science (pp. 123-140). Springer, Cham.‏. 58 | """ 59 | 60 | if is_directed: 61 | G = nx.DiGraph() 62 | dynamic_graph = dn.DynDiGraph() 63 | else: 64 | G = nx.Graph() 65 | dynamic_graph = dn.DynGraph() 66 | 67 | edges = list(get_edges_iterator(n, is_directed)) 68 | # Init graph nodes 69 | G.add_nodes_from(range(n)) 70 | dynamic_graph.add_nodes_from(range(n)) 71 | 72 | list_of_graph_snapshots = list() 73 | 74 | # When t=0: 75 | if up_rate <= 0: 76 | list_of_graph_snapshots = [G] * steps 77 | 78 | elif up_rate >= 1: 79 | G.add_edges_from(edges) 80 | list_of_graph_snapshots = [G] * steps 81 | 82 | else: # up-rate is in between (0,1) 83 | for e in edges: 84 | if random() < up_rate: 85 | G.add_edge(*e) 86 | list_of_graph_snapshots.append(G) 87 | 88 | for t in range(1, steps): 89 | G_t = nx.Graph() 90 | edges = get_edges_iterator(n, is_directed) 91 | 92 | for e in edges: 93 | if list_of_graph_snapshots[t - 1].has_edge( 94 | *e 95 | ): # check if edge is in graph in previous step 96 | if random() > down_rate: 97 | G_t.add_edge(*e) 98 | else: # un-exist edge 99 | if random() < up_rate: 100 | G_t.add_edge(*e) 101 | list_of_graph_snapshots.append(G_t) 102 | 103 | for t, graph in enumerate(list_of_graph_snapshots): 104 | dynamic_graph.add_interactions_from(graph.edges(data=True), t=t) 105 | 106 | if write_to_file: 107 | dn.readwrite.edgelist.write_interactions( 108 | dynamic_graph, f"{output_file_name}.edge_list" 109 | ) 110 | dn.readwrite.edgelist.write_snapshots( 111 | dynamic_graph, f"{output_file_name}.snapshot" 112 | ) 113 | 114 | return dynamic_graph 115 | -------------------------------------------------------------------------------- /random_graph_generator/fast_dynamic_random_graph.py: -------------------------------------------------------------------------------- 1 | import time 2 | from pathlib import Path 3 | 4 | import numpy as np 5 | import pandas as pd 6 | from tqdm import tqdm 7 | 8 | 9 | def adj_matrix_to_df(adj_t: np.ndarray, step: int = None): 10 | row, col = np.where(adj_t) 11 | coo = np.rec.fromarrays([row, col], names="source destination".split()) 12 | df = pd.DataFrame.from_records(coo, columns=["source", "destination"]) 13 | if step is not None: 14 | df["datetime"] = step 15 | df = df[["datetime", "source", "destination"]] 16 | return df 17 | 18 | 19 | def get_binary_matrix_mask(n: int, rate_to_one: float): 20 | return np.random.choice([0, 1], size=(n, n), p=[1 - rate_to_one, rate_to_one]) 21 | 22 | 23 | def fast_dynamic_er_random_graph( 24 | n: int, 25 | steps: int, 26 | up_rate: float, 27 | down_rate: float, 28 | write_to_csv: bool = True, 29 | is_directed: bool = False, 30 | output_file_name: str = None, 31 | ): 32 | """Returns a $G_{n,mu, lambda}$ dynamic random graph, also known as an dynamic Erdős-Rényi graph. 33 | The $G_{n,\mu, \lambda}$ model chooses to create inexist edges with probabilty $\mu$ (also known as up-rate) 34 | and remove exist edges with probability $\lambda$ (also known as doen-rate). 35 | Parameters 36 | ---------- 37 | n : int 38 | The number of nodes. 39 | steps: int 40 | The number of timestamps in the temporal network. The length of the network in the time axis. 41 | up_rate : float 42 | Probability for edge creation. 43 | down_rate: float 44 | Probability of edge removal 45 | write_to_csv: bool, optional (default=False) 46 | False will return pandas dataframe, True will write the results to CSV (preferred for huge networks) 47 | output_file_name : str 48 | Destination for csv file, relevant only if ''write_to_csv'' is True 49 | is_directed : bool, optional (default=False) 50 | If True, this function returns a directed graph. 51 | See Also 52 | -------- 53 | gnp_random_graph 54 | fast_gnp_random_graph 55 | 56 | Notes 57 | ----- 58 | This algorithm runs in $O(?)$ time. 59 | 60 | References 61 | ---------- 62 | .. [1] Mandjes, M., Starreveld, N., Bekker, R., & Spreij, P. (2019). Dynamic Erdős-Rényi Graphs. 63 | In Computing and Software Science (pp. 123-140). Springer, Cham.‏. 64 | """ 65 | if write_to_csv is True and not output_file_name: 66 | output_file_name = ( 67 | Path("data") 68 | / f"random_undirected_er_{n}_nodes_{steps}_steps_{up_rate}_up_{down_rate}_down_{time.time()}.csv" 69 | ) 70 | # Init t=0 adjacency matrix 71 | adj_t = np.zeros((n, n)) 72 | 73 | # $A_{t+1} = (A_t \cdot (1-R_d)) + (1-A_t) \cdot R_u)$ 74 | # S.t. A_t is the adj matrix, R_d is a binary random matrix with prob of down-rate of non-zero values. 75 | # R_u is a binary random matrix with prob of up-rate of non-zero values 76 | df_adj = pd.DataFrame(columns=[["datetime", "source", "destination"]]) 77 | for step in tqdm(range(steps)): 78 | down_rate_mask = get_binary_matrix_mask(n, down_rate) 79 | up_rate_mask = get_binary_matrix_mask(n, up_rate) 80 | 81 | old_edges_survived = adj_t * (1 - down_rate_mask) 82 | new_edges_created = (1 - adj_t) * up_rate_mask 83 | 84 | adj_t = old_edges_survived + new_edges_created 85 | # np.fill_diagonal(adj_t, 0) # remove self-edges 86 | if is_directed: 87 | df_compressed_adj_t = adj_matrix_to_df(adj_t, step) 88 | else: # make the adj matrix symmetric again 89 | df_compressed_adj_t = adj_matrix_to_df(np.tril(adj_t), step) 90 | 91 | if df_adj.size == 0: 92 | df_adj = df_compressed_adj_t.copy() 93 | else: 94 | df_adj = pd.concat([df_adj, df_compressed_adj_t]) 95 | 96 | if write_to_csv: 97 | if df_adj.size > 100000: 98 | if output_file_name.is_file(): 99 | df_adj.to_csv(output_file_name, mode="a", header=False, index=False) 100 | else: 101 | df_adj.to_csv(output_file_name, mode="a", header=True, index=False) 102 | 103 | df_adj = pd.DataFrame(columns=[["datetime", "source", "destination"]]) 104 | 105 | if write_to_csv: 106 | df_adj.to_csv(output_file_name, mode="a", header=False, index=False) 107 | 108 | if not write_to_csv: 109 | return df_adj 110 | 111 | else: 112 | return 113 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | tqdm==4.62.0 2 | numpy==1.20.3 3 | joblib==1.0.1 4 | pytest==6.2.4 5 | dynetx==0.3.0 6 | pandas==1.3.2 7 | networkx==2.6.2 8 | -------------------------------------------------------------------------------- /tests/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ScanLab-ossi/DynamicRandomGraphs/31ae70aeff7f2d4325cd3b43efeeb9cc9a299667/tests/__init__.py -------------------------------------------------------------------------------- /tests/conftest.py: -------------------------------------------------------------------------------- 1 | import pytest 2 | 3 | 4 | def pytest_addoption(parser): 5 | parser.addoption( 6 | "--runslow", action="store_true", default=False, help="run slow tests" 7 | ) 8 | 9 | 10 | def pytest_configure(config): 11 | config.addinivalue_line("markers", "slow: mark test as slow to run") 12 | 13 | 14 | def pytest_collection_modifyitems(config, items): 15 | if config.getoption("--runslow"): 16 | # --runslow given in cli: do not skip slow tests 17 | return 18 | skip_slow = pytest.mark.skip(reason="need --runslow option to run") 19 | for item in items: 20 | if "slow" in item.keywords: 21 | item.add_marker(skip_slow) 22 | -------------------------------------------------------------------------------- /tests/test_fast_random_graph.py: -------------------------------------------------------------------------------- 1 | import random 2 | import statistics 3 | from os import path, write 4 | from typing import Optional 5 | 6 | import numpy as np 7 | import pandas as pd 8 | import pytest 9 | import random_graph_generator.fast_dynamic_random_graph as fdrg 10 | import tests.utils as utils 11 | from pandas._testing import assert_frame_equal 12 | 13 | 14 | def test_adj_matrix_to_df_zeros_matrix(): 15 | s = np.zeros((10, 10)) 16 | test_df = fdrg.adj_matrix_to_df(adj_t=s, step=1) 17 | expected_df = pd.DataFrame(columns=["datetime", "source", "destination"]) 18 | assert_frame_equal(test_df, expected_df, check_dtype=False, check_index_type=False) 19 | 20 | 21 | def test_adj_matrix_to_df_not_empty_matrix(): 22 | s = np.zeros((10, 10)) 23 | s[0, 1] = 1 24 | test_df = fdrg.adj_matrix_to_df(adj_t=s, step=1) 25 | 26 | data = {"datetime": [1], "source": [0], "destination": [1]} 27 | expected_df = pd.DataFrame.from_dict(data) 28 | assert_frame_equal(test_df, expected_df, check_dtype=False, check_index_type=False) 29 | 30 | 31 | def get_delta_between_fast_random_graph_density_and_expected( 32 | write_to_csv: bool = False, 33 | is_directed: bool = False, 34 | output_file_name: Optional[str] = None, 35 | ): 36 | nodes = random.randint(25, 100) 37 | steps = random.randint(25, 100) 38 | alpha = random.uniform(0.0001, 0.1) 39 | beta = random.uniform(0.0001, 0.9999) 40 | df = fdrg.fast_dynamic_er_random_graph( 41 | nodes, steps, alpha, beta, write_to_csv, is_directed, output_file_name 42 | ) 43 | total_edges = len(df.index) 44 | max_number_edges = utils.get_number_of_potential_edges(nodes, is_directed) 45 | real_density = total_edges / (max_number_edges * steps) 46 | expected_density = utils.get_expected_er_model_density(alpha, beta) 47 | return real_density - expected_density 48 | 49 | 50 | def get_avg_delta_and_std(iterations: int, is_directed: bool = False): 51 | delta_list = list() 52 | for _ in range(iterations): 53 | delta_list.append( 54 | get_delta_between_fast_random_graph_density_and_expected( 55 | write_to_csv=False, is_directed=is_directed 56 | ) 57 | ) 58 | avg_delta = sum(delta_list) / len(delta_list) 59 | std_delta = statistics.stdev(delta_list) 60 | return avg_delta, std_delta 61 | 62 | 63 | # def test_create_fast_undirected_graph_density(): 64 | # avg_delta, std_delta = get_avg_delta_and_std(iterations=10, is_directed=False) 65 | # assert avg_delta < 0.1 66 | # assert std_delta < 0.15 67 | 68 | 69 | @pytest.mark.slow 70 | def test_create_fast_undirected_graph_density_slow(): 71 | avg_delta, std_delta = get_avg_delta_and_std(iterations=200, is_directed=False) 72 | assert avg_delta < 0.1 73 | assert std_delta < 0.15 74 | 75 | 76 | @pytest.mark.slow 77 | def test_create_fast_directed_graph_density_slow(): 78 | avg_delta, std_delta = get_avg_delta_and_std(iterations=200, is_directed=True) 79 | assert avg_delta < 0.1 80 | assert std_delta < 0.15 81 | 82 | 83 | def test_get_binary_matrix_mask_size(): 84 | arr = fdrg.get_binary_matrix_mask(10, 0.5) 85 | assert arr.shape == (10, 10) 86 | arr = fdrg.get_binary_matrix_mask(100, 0.3) 87 | assert arr.shape == (100, 100) 88 | 89 | 90 | def test_get_binary_matrix_mask_ones_counter(): 91 | nodes = 100 92 | x = nodes ** 2 93 | p = 0.3 94 | arr = fdrg.get_binary_matrix_mask(nodes, p) 95 | counter = np.sum(arr) 96 | expected_count = p * x 97 | std = np.sqrt(x * p * (1 - p)) # std of binomial distribution 98 | assert expected_count + std > counter > expected_count - std 99 | 100 | 101 | def test_create_graph_writing(tmpdir): 102 | file_ = tmpdir.join("output.csv") 103 | df = fdrg.fast_dynamic_er_random_graph( 104 | n=50, 105 | steps=50, 106 | up_rate=0.1, 107 | down_rate=0.5, 108 | write_to_csv=True, 109 | is_directed=False, 110 | output_file_name=file_, 111 | ) 112 | assert path.exists(file_) 113 | -------------------------------------------------------------------------------- /tests/test_networkx.py: -------------------------------------------------------------------------------- 1 | import random 2 | import statistics 3 | 4 | import dynetx as dn 5 | import pytest 6 | import random_graph_generator.dynamic_random_graph as drg 7 | import tests.utils as utils 8 | 9 | 10 | def test_get_edges_iterator_directed(): 11 | edges = drg.get_edges_iterator(3, directed=True) 12 | assert list(edges) == [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)] 13 | 14 | 15 | def test_get_edges_iterator_undirected(): 16 | edges = drg.get_edges_iterator(3, directed=False) 17 | assert list(edges) == [(0, 1), (0, 2), (1, 2)] 18 | 19 | 20 | def test_create_full_graph_directed(): 21 | G_ref = dn.DynDiGraph() 22 | G_ref.add_nodes_from(range(3)) 23 | for t in range(5): 24 | G_ref.add_interactions_from( 25 | [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)], t=t 26 | ) 27 | 28 | G = drg.dynamic_er_random_graph(3, 5, 1, 0.8, None, True) 29 | assert G.order() == G_ref.order() == 3 # number of nodes 30 | for i in range(5): # number of edges per t 31 | assert ( 32 | dn.number_of_interactions(G, t=i) 33 | == dn.number_of_interactions(G_ref, t=0) 34 | == 6 35 | ) 36 | 37 | 38 | def test_create_full_graph_undirected(): 39 | G_ref = dn.DynDiGraph() 40 | G_ref.add_nodes_from(range(3)) 41 | for t in range(5): 42 | G_ref.add_interactions_from([(0, 1), (0, 2), (1, 2)], t=t) 43 | 44 | G = drg.dynamic_er_random_graph(3, 5, 1, 0.8, None, False) 45 | assert G.order() == G_ref.order() == 3 # number of nodes 46 | for i in range(5): # number of edges per t 47 | assert ( 48 | dn.number_of_interactions(G, t=i) 49 | == dn.number_of_interactions(G_ref, t=0) 50 | == 3 51 | ) 52 | 53 | 54 | def test_create_empty_graph_directed(): 55 | G = drg.dynamic_er_random_graph(3, 5, 0, 0.5, None, True) 56 | assert G.order() == 3 # number of nodes 57 | for i in range(5): # number of edges per t 58 | assert dn.number_of_interactions(G, t=i) == 0 59 | 60 | 61 | def test_create_empty_graph_undirected(): 62 | G = drg.dynamic_er_random_graph(3, 5, 0, 0.5, None, False) 63 | assert G.order() == 3 # number of nodes 64 | for i in range(5): # number of edges per t 65 | assert dn.number_of_interactions(G, t=i) == 0 66 | 67 | 68 | def get_delta_between_nx_random_graph_density_and_expected(is_directed=False): 69 | nodes = random.randint(25, 50) 70 | steps = random.randint(25, 50) 71 | alpha = random.uniform(0.0001, 0.1) 72 | beta = random.uniform(0.0001, 0.9999) 73 | G = drg.dynamic_er_random_graph(nodes, steps, alpha, beta, None, False) 74 | total_edges = sum(dn.number_of_interactions(G, t=i) for i in range(steps)) 75 | max_number_edges = utils.get_number_of_potential_edges(nodes, is_directed) 76 | real_density = total_edges / (max_number_edges * steps) 77 | expected_density = utils.get_expected_er_model_density(alpha, beta) 78 | return real_density - expected_density 79 | 80 | 81 | def test_create_undirected_graph_density(): 82 | delta_list = list() 83 | for i in range(10): 84 | delta = get_delta_between_nx_random_graph_density_and_expected() 85 | delta_list.append(delta) 86 | avg_delta = sum(delta_list) / len(delta_list) 87 | std_delta = statistics.stdev(delta_list) 88 | assert avg_delta < 0.1 89 | assert std_delta < 0.15 90 | 91 | 92 | @pytest.mark.slow 93 | def test_create_undirected_graph_density_slow(): 94 | delta_list = list() 95 | for i in range(100): 96 | delta = get_delta_between_nx_random_graph_density_and_expected() 97 | delta_list.append(delta) 98 | avg_delta = sum(delta_list) / len(delta_list) 99 | std_delta = statistics.stdev(delta_list) 100 | assert avg_delta < 0.1 101 | assert std_delta < 0.15 102 | 103 | 104 | def test_create_directed_graph_density(): 105 | delta_list = list() 106 | for i in range(2): 107 | delta = get_delta_between_nx_random_graph_density_and_expected(is_directed=True) 108 | delta_list.append(delta) 109 | avg_delta = sum(delta_list) / len(delta_list) 110 | std_delta = statistics.stdev(delta_list) 111 | assert avg_delta < 0.1 112 | assert std_delta < 0.15 113 | 114 | 115 | @pytest.mark.slow 116 | def test_create_directed_graph_density_slow(): 117 | delta_list = list() 118 | for i in range(100): 119 | delta = get_delta_between_nx_random_graph_density_and_expected(is_directed=True) 120 | delta_list.append(delta) 121 | avg_delta = sum(delta_list) / len(delta_list) 122 | std_delta = statistics.stdev(delta_list) 123 | assert avg_delta < 0.1 124 | assert std_delta < 0.15 125 | -------------------------------------------------------------------------------- /tests/utils.py: -------------------------------------------------------------------------------- 1 | def get_number_of_potential_edges(nodes: int, is_directed: bool): 2 | if is_directed: 3 | return nodes * (nodes - 1) / 2 4 | else: 5 | return nodes * (nodes - 1) 6 | 7 | 8 | def get_expected_er_model_density(alpha: float, beta: float): 9 | return alpha / (alpha + beta) 10 | --------------------------------------------------------------------------------