├── .gitattributes ├── .github └── workflows │ └── ci.yml ├── .gitignore ├── LICENSE ├── README.md ├── data ├── flight │ ├── brazil-airports.edgelist │ ├── europe-airports.edgelist │ ├── labels-brazil-airports.txt │ ├── labels-europe-airports.txt │ ├── labels-usa-airports.txt │ └── usa-airports.edgelist └── wiki │ ├── Wiki_category.txt │ ├── Wiki_edgelist.txt │ └── wiki_labels.txt ├── examples ├── alias.py ├── deepwalk_wiki.py ├── line_wiki.py ├── node2vec_flight.py ├── node2vec_wiki.py ├── sdne_wiki.py └── struc2vec_flight.py ├── ge ├── __init__.py ├── alias.py ├── classify.py ├── models │ ├── __init__.py │ ├── deepwalk.py │ ├── line.py │ ├── node2vec.py │ ├── sdne.py │ └── struc2vec.py ├── utils.py └── walker.py ├── pics ├── code.png ├── deepctrbot.png ├── edge_list.png └── weichennote.png ├── setup.py └── tests ├── Wiki_edgelist.txt ├── __init__.py ├── deepwalk_test.py ├── line_test.py ├── node2vec_test.py ├── sdne_test.py └── struct2vec_test.py /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /.github/workflows/ci.yml: -------------------------------------------------------------------------------- 1 | name: CI 2 | 3 | on: 4 | push: 5 | path: 6 | - 'ge/*' 7 | - 'tests/*' 8 | pull_request: 9 | path: 10 | - 'ge/*' 11 | - 'tests/*' 12 | 13 | jobs: 14 | build: 15 | 16 | runs-on: ubuntu-latest 17 | timeout-minutes: 180 18 | strategy: 19 | matrix: 20 | python-version: [3.6,3.7,3.8] 21 | tf-version: [1.4.0,1.15.0,2.5.0,2.6.0,2.7.0,2.8.0,2.9.0] 22 | 23 | exclude: 24 | - python-version: 3.7 25 | tf-version: 1.4.0 26 | - python-version: 3.7 27 | tf-version: 1.15.0 28 | - python-version: 3.8 29 | tf-version: 1.4.0 30 | - python-version: 3.8 31 | tf-version: 1.14.0 32 | - python-version: 3.8 33 | tf-version: 1.15.0 34 | - python-version: 3.6 35 | tf-version: 2.7.0 36 | - python-version: 3.6 37 | tf-version: 2.8.0 38 | - python-version: 3.6 39 | tf-version: 2.9.0 40 | - python-version: 3.9 41 | tf-version: 1.4.0 42 | - python-version: 3.9 43 | tf-version: 1.15.0 44 | - python-version: 3.9 45 | tf-version: 2.2.0 46 | steps: 47 | 48 | - uses: actions/checkout@v3 49 | 50 | - name: Setup python environment 51 | uses: actions/setup-python@v4 52 | with: 53 | python-version: ${{ matrix.python-version }} 54 | 55 | - name: Install dependencies 56 | run: | 57 | pip3 install -q tensorflow==${{ matrix.tf-version }} 58 | pip install -q protobuf==3.19.0 59 | pip install -q requests 60 | pip install -e . 61 | - name: Test with pytest 62 | timeout-minutes: 180 63 | run: | 64 | pip install -q pytest 65 | pip install -q pytest-cov 66 | pip install -q python-coveralls 67 | pytest --cov=ge --cov-report=xml 68 | - name: Upload coverage to Codecov 69 | uses: codecov/codecov-action@v3.1.0 70 | with: 71 | token: ${{secrets.CODECOV_TOKEN}} 72 | file: ./coverage.xml 73 | flags: pytest 74 | name: py${{ matrix.python-version }}-tf${{ matrix.tf-version }} 75 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .nox/ 42 | .coverage 43 | .coverage.* 44 | .cache 45 | nosetests.xml 46 | coverage.xml 47 | *.cover 48 | .hypothesis/ 49 | .pytest_cache/ 50 | 51 | # Translations 52 | *.mo 53 | *.pot 54 | 55 | # Django stuff: 56 | *.log 57 | local_settings.py 58 | db.sqlite3 59 | 60 | # Flask stuff: 61 | instance/ 62 | .webassets-cache 63 | 64 | # Scrapy stuff: 65 | .scrapy 66 | 67 | # Sphinx documentation 68 | docs/_build/ 69 | 70 | # PyBuilder 71 | target/ 72 | 73 | # Jupyter Notebook 74 | .ipynb_checkpoints 75 | 76 | # IPython 77 | profile_default/ 78 | ipython_config.py 79 | 80 | # pyenv 81 | .python-version 82 | 83 | # celery beat schedule file 84 | celerybeat-schedule 85 | 86 | # SageMath parsed files 87 | *.sage.py 88 | 89 | # Environments 90 | .env 91 | .venv 92 | env/ 93 | venv/ 94 | ENV/ 95 | env.bak/ 96 | venv.bak/ 97 | 98 | # Spyder project settings 99 | .spyderproject 100 | .spyproject 101 | 102 | # Rope project settings 103 | .ropeproject 104 | 105 | # mkdocs documentation 106 | /site 107 | 108 | # mypy 109 | .mypy_cache/ 110 | .dmypy.json 111 | dmypy.json 112 | 113 | # Pyre type checker 114 | .pyre/ 115 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 Weichen Shen 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # GraphEmbedding 2 | 3 | [![GitHub Issues](https://img.shields.io/github/issues/shenweichen/graphembedding.svg 4 | )](https://github.com/shenweichen/graphembedding/issues) 5 | ![CI status](https://github.com/shenweichen/graphembedding/workflows/CI/badge.svg) 6 | [![codecov](https://codecov.io/gh/shenweichen/graphembedding/branch/master/graph/badge.svg)](https://codecov.io/gh/shenweichen/graphembedding) 7 | [![Codacy Badge](https://app.codacy.com/project/badge/Grade/c46407f5931f40048e28860dccf7dabc)](https://www.codacy.com/gh/shenweichen/GraphEmbedding/dashboard?utm_source=github.com&utm_medium=referral&utm_content=shenweichen/GraphEmbedding&utm_campaign=Badge_Grade) 8 | [![Disscussion](https://img.shields.io/badge/chat-wechat-brightgreen?style=flat)](./README.md#disscussiongroup--related-projects) 9 | 10 | [comment]: <> ([![License](https://img.shields.io/github/license/shenweichen/graphembedding.svg)](https://github.com/shenweichen/graphembedding/blob/master/LICENSE)) 11 | 12 | # Method 13 | 14 | 15 | | Model | Paper | Note | 16 | | :-------: | :------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------ | 17 | | DeepWalk | [KDD 2014][DeepWalk: Online Learning of Social Representations](http://www.perozzi.net/publications/14_kdd_deepwalk.pdf) | [【Graph Embedding】DeepWalk：算法原理，实现和应用](https://zhuanlan.zhihu.com/p/56380812) | 18 | | LINE | [WWW 2015][LINE: Large-scale Information Network Embedding](https://arxiv.org/pdf/1503.03578.pdf) | [【Graph Embedding】LINE：算法原理，实现和应用](https://zhuanlan.zhihu.com/p/56478167) | 19 | | Node2Vec | [KDD 2016][node2vec: Scalable Feature Learning for Networks](https://www.kdd.org/kdd2016/papers/files/rfp0218-groverA.pdf) | [【Graph Embedding】Node2Vec：算法原理，实现和应用](https://zhuanlan.zhihu.com/p/56542707) | 20 | | SDNE | [KDD 2016][Structural Deep Network Embedding](https://www.kdd.org/kdd2016/papers/files/rfp0191-wangAemb.pdf) | [【Graph Embedding】SDNE：算法原理，实现和应用](https://zhuanlan.zhihu.com/p/56637181) | 21 | | Struc2Vec | [KDD 2017][struc2vec: Learning Node Representations from Structural Identity](https://arxiv.org/pdf/1704.03165.pdf) | [【Graph Embedding】Struc2Vec：算法原理，实现和应用](https://zhuanlan.zhihu.com/p/56733145) | 22 | 23 | 24 | # How to run examples 25 | 1. clone the repo and make sure you have installed `tensorflow` or `tensorflow-gpu` on your local machine. 26 | 2. run following commands 27 | ```bash 28 | python setup.py install 29 | cd examples 30 | python deepwalk_wiki.py 31 | ``` 32 | 33 | ## DisscussionGroup & Related Projects 34 | 35 | 36 | 37 | 38 | 44 | 50 | 58 | 59 |
39 | 公众号：浅梦学习笔记

40 | 41 | 42 | 43 | 		45 | 微信：deepctrbot

46 | 47 | 48 | 49 | 		51 | 57 |
60 | 61 | 62 | # Usage 63 | The design and implementation follows simple principles(**graph in,embedding out**) as much as possible. 64 | ## Input format 65 | we use `networkx`to create graphs.The input of networkx graph is as follows: 66 | `node1 node2 ` 67 | 68 | ![](./pics/edge_list.png) 69 | ## DeepWalk 70 | 71 | ```python 72 | G = nx.read_edgelist('../data/wiki/Wiki_edgelist.txt',create_using=nx.DiGraph(),nodetype=None,data=[('weight',int)])# Read graph 73 | 74 | model = DeepWalk(G,walk_length=10,num_walks=80,workers=1)#init model 75 | model.train(window_size=5,iter=3)# train model 76 | embeddings = model.get_embeddings()# get embedding vectors 77 | ``` 78 | 79 | ## LINE 80 | 81 | ```python 82 | G = nx.read_edgelist('../data/wiki/Wiki_edgelist.txt',create_using=nx.DiGraph(),nodetype=None,data=[('weight',int)])#read graph 83 | 84 | model = LINE(G,embedding_size=128,order='second') #init model,order can be ['first','second','all'] 85 | model.train(batch_size=1024,epochs=50,verbose=2)# train model 86 | embeddings = model.get_embeddings()# get embedding vectors 87 | ``` 88 | ## Node2Vec 89 | ```python 90 | G=nx.read_edgelist('../data/wiki/Wiki_edgelist.txt', 91 | create_using = nx.DiGraph(), nodetype = None, data = [('weight', int)])#read graph 92 | 93 | model = Node2Vec(G, walk_length = 10, num_walks = 80,p = 0.25, q = 4, workers = 1)#init model 94 | model.train(window_size = 5, iter = 3)# train model 95 | embeddings = model.get_embeddings()# get embedding vectors 96 | ``` 97 | ## SDNE 98 | 99 | ```python 100 | G = nx.read_edgelist('../data/wiki/Wiki_edgelist.txt',create_using=nx.DiGraph(),nodetype=None,data=[('weight',int)])#read graph 101 | 102 | model = SDNE(G,hidden_size=[256,128]) #init model 103 | model.train(batch_size=3000,epochs=40,verbose=2)# train model 104 | embeddings = model.get_embeddings()# get embedding vectors 105 | ``` 106 | 107 | ## Struc2Vec 108 | 109 | 110 | ```python 111 | G = nx.read_edgelist('../data/flight/brazil-airports.edgelist',create_using=nx.DiGraph(),nodetype=None,data=[('weight',int)])#read graph 112 | 113 | model = Struc2Vec(G, 10, 80, workers=4, verbose=40, ) #init model 114 | model.train(window_size = 5, iter = 3)# train model 115 | embeddings = model.get_embeddings()# get embedding vectors 116 | ``` 117 | -------------------------------------------------------------------------------- /data/flight/brazil-airports.edgelist: -------------------------------------------------------------------------------- 1 | 7 77 2 | 29 50 3 | 3 35 4 | 9 84 5 | 25 82 6 | 6 28 7 | 64 51 8 | 108 74 9 | 19 4 10 | 2 84 11 | 7 25 12 | 56 25 13 | 24 66 14 | 23 26 15 | 30 51 16 | 10 61 17 | 50 84 18 | 65 84 19 | 53 53 20 | 9 0 21 | 30 15 22 | 45 35 23 | 10 7 24 | 87 52 25 | 106 19 26 | 70 53 27 | 58 130 28 | 6 98 29 | 103 80 30 | 7 19 31 | 36 40 32 | 25 15 33 | 15 4 34 | 4 50 35 | 2 27 36 | 7 98 37 | 9 123 38 | 1 54 39 | 58 65 40 | 4 5 41 | 96 77 42 | 9 127 43 | 50 27 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822 | 41 69 823 | 126 66 824 | 3 7 825 | 66 123 826 | 99 27 827 | 30 13 828 | 28 5 829 | 2 48 830 | 85 84 831 | 31 8 832 | 5 5 833 | 79 2 834 | 9 66 835 | 51 39 836 | 6 10 837 | 19 41 838 | 36 119 839 | 2 42 840 | 4 69 841 | 3 51 842 | 25 66 843 | 10 98 844 | 19 19 845 | 2 68 846 | 58 25 847 | 7 41 848 | 56 9 849 | 7 84 850 | 33 25 851 | 25 100 852 | 90 90 853 | 3 64 854 | 45 25 855 | 31 71 856 | 24 24 857 | 62 5 858 | 45 51 859 | 45 27 860 | 0 1 861 | 51 120 862 | 53 26 863 | 27 27 864 | 25 63 865 | 58 87 866 | 6 18 867 | 66 4 868 | 5 14 869 | 70 84 870 | 3 18 871 | 97 84 872 | 82 82 873 | 45 78 874 | 66 66 875 | 31 31 876 | 55 50 877 | 3 56 878 | 27 87 879 | 24 83 880 | 66 6 881 | 6 39 882 | 21 21 883 | 4 51 884 | 60 48 885 | 84 53 886 | 46 7 887 | 59 45 888 | 25 109 889 | 6 57 890 | 3 73 891 | 60 46 892 | 67 71 893 | 28 71 894 | 9 25 895 | 5 67 896 | 7 102 897 | 36 7 898 | 10 30 899 | 25 30 900 | 114 25 901 | 68 28 902 | 70 60 903 | 0 24 904 | 74 51 905 | 70 48 906 | 7 120 907 | 1 5 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1074 | 2 58 1075 | -------------------------------------------------------------------------------- /data/flight/labels-brazil-airports.txt: -------------------------------------------------------------------------------- 1 | node label 2 | 0 0 3 | 1 0 4 | 2 0 5 | 3 0 6 | 4 0 7 | 5 0 8 | 6 0 9 | 7 0 10 | 8 1 11 | 9 0 12 | 10 0 13 | 11 3 14 | 12 3 15 | 13 2 16 | 14 3 17 | 15 0 18 | 16 3 19 | 17 3 20 | 18 1 21 | 19 1 22 | 20 3 23 | 21 0 24 | 22 1 25 | 23 1 26 | 24 0 27 | 25 0 28 | 26 1 29 | 27 0 30 | 28 0 31 | 29 1 32 | 30 0 33 | 31 0 34 | 32 2 35 | 33 1 36 | 34 3 37 | 35 3 38 | 36 0 39 | 37 2 40 | 38 1 41 | 39 1 42 | 40 1 43 | 41 0 44 | 42 1 45 | 43 2 46 | 44 3 47 | 45 1 48 | 46 2 49 | 47 2 50 | 48 1 51 | 49 1 52 | 50 0 53 | 51 0 54 | 52 1 55 | 53 0 56 | 54 1 57 | 55 2 58 | 56 2 59 | 57 2 60 | 58 0 61 | 59 2 62 | 60 1 63 | 61 0 64 | 62 2 65 | 63 1 66 | 64 0 67 | 65 1 68 | 66 0 69 | 67 3 70 | 68 3 71 | 69 0 72 | 70 1 73 | 71 0 74 | 72 3 75 | 73 3 76 | 74 0 77 | 75 1 78 | 76 2 79 | 77 0 80 | 78 2 81 | 79 0 82 | 80 1 83 | 81 2 84 | 82 1 85 | 83 2 86 | 84 1 87 | 85 2 88 | 86 2 89 | 87 1 90 | 88 3 91 | 89 2 92 | 90 2 93 | 91 2 94 | 92 2 95 | 93 1 96 | 94 1 97 | 95 3 98 | 96 1 99 | 97 1 100 | 98 1 101 | 99 2 102 | 100 2 103 | 101 2 104 | 102 2 105 | 103 2 106 | 104 3 107 | 105 3 108 | 106 2 109 | 107 3 110 | 108 3 111 | 109 3 112 | 110 3 113 | 111 3 114 | 112 3 115 | 113 2 116 | 114 3 117 | 115 3 118 | 116 3 119 | 117 3 120 | 118 3 121 | 119 2 122 | 120 3 123 | 121 3 124 | 122 3 125 | 123 2 126 | 124 3 127 | 125 2 128 | 126 3 129 | 127 1 130 | 128 3 131 | 129 3 132 | 130 2 133 | -------------------------------------------------------------------------------- /data/flight/labels-europe-airports.txt: -------------------------------------------------------------------------------- 1 | node label 2 | 0 1 3 | 1 1 4 | 2 2 5 | 3 1 6 | 4 0 7 | 5 0 8 | 6 0 9 | 7 0 10 | 8 0 11 | 9 0 12 | 10 0 13 | 11 0 14 | 12 0 15 | 13 1 16 | 14 0 17 | 15 0 18 | 16 0 19 | 17 1 20 | 18 0 21 | 19 0 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1094 | 1093 5 1095 | 1094 5 1096 | 1095 5 1097 | 1096 5 1098 | 1097 5 1099 | 1098 5 1100 | 1099 5 1101 | 1100 5 1102 | 1101 5 1103 | 1102 5 1104 | 1103 5 1105 | 1104 5 1106 | 1105 5 1107 | 1106 5 1108 | 1107 5 1109 | 1108 5 1110 | 1109 5 1111 | 1110 5 1112 | 1111 5 1113 | 1112 5 1114 | 1113 5 1115 | 1114 5 1116 | 1115 5 1117 | 1116 5 1118 | 1117 5 1119 | 1118 5 1120 | 1119 5 1121 | 1120 5 1122 | 1121 5 1123 | 1122 5 1124 | 1123 5 1125 | 1124 5 1126 | 1125 5 1127 | 1126 5 1128 | 1127 5 1129 | 1128 5 1130 | 1129 5 1131 | 1130 5 1132 | 1131 5 1133 | 1132 5 1134 | 1133 5 1135 | 1134 5 1136 | 1135 5 1137 | 1136 5 1138 | 1137 5 1139 | 1138 5 1140 | 1139 5 1141 | 1140 5 1142 | 1141 5 1143 | 1142 5 1144 | 1143 5 1145 | 1144 5 1146 | 1145 5 1147 | 1146 5 1148 | 1147 5 1149 | 1148 5 1150 | 1149 5 1151 | 1150 5 1152 | 1151 5 1153 | 1152 5 1154 | 1153 5 1155 | 1154 5 1156 | 1155 5 1157 | 1156 5 1158 | 1157 5 1159 | 1158 5 1160 | 1159 5 1161 | 1160 5 1162 | 1161 5 1163 | 1162 5 1164 | 1163 5 1165 | 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1235 | 1234 11 1236 | 1235 11 1237 | 1236 11 1238 | 1237 11 1239 | 1238 11 1240 | 1239 11 1241 | 1240 11 1242 | 1241 11 1243 | 1242 11 1244 | 1243 11 1245 | 1244 11 1246 | 1245 11 1247 | 1246 11 1248 | 1247 11 1249 | 1248 11 1250 | 1249 11 1251 | 1250 11 1252 | 1251 11 1253 | 1252 11 1254 | 1253 11 1255 | 1254 11 1256 | 1255 11 1257 | 1256 11 1258 | 1257 11 1259 | 1258 11 1260 | 1259 11 1261 | 1260 11 1262 | 1261 11 1263 | 1262 11 1264 | 1263 11 1265 | 1264 11 1266 | 1265 11 1267 | 1266 11 1268 | 1267 11 1269 | 1268 11 1270 | 1269 11 1271 | 1270 11 1272 | 1271 11 1273 | 1272 11 1274 | 1273 11 1275 | 1274 11 1276 | 1275 11 1277 | 1276 11 1278 | 1277 11 1279 | 1278 11 1280 | 1279 11 1281 | 1280 11 1282 | 1281 11 1283 | 1282 11 1284 | 1283 11 1285 | 1284 11 1286 | 1285 11 1287 | 1286 11 1288 | 1287 11 1289 | 1288 11 1290 | 1289 11 1291 | 1290 11 1292 | 1291 11 1293 | 1292 11 1294 | 1293 11 1295 | 1294 11 1296 | 1295 11 1297 | 1296 11 1298 | 1297 11 1299 | 1298 11 1300 | 1299 11 1301 | 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1368 | 1367 11 1369 | 1368 11 1370 | 1369 11 1371 | 1370 11 1372 | 1371 11 1373 | 1372 11 1374 | 1373 11 1375 | 1374 11 1376 | 1375 11 1377 | 1376 11 1378 | 1377 11 1379 | 1378 11 1380 | 1379 11 1381 | 1380 11 1382 | 1381 11 1383 | 1382 11 1384 | 1383 11 1385 | 1384 11 1386 | 1385 11 1387 | 1386 4 1388 | 1387 4 1389 | 1388 4 1390 | 1389 4 1391 | 1390 4 1392 | 1391 4 1393 | 1392 4 1394 | 1393 4 1395 | 1394 4 1396 | 1395 4 1397 | 1396 3 1398 | 1397 3 1399 | 1398 3 1400 | 1399 3 1401 | 1400 3 1402 | 1401 3 1403 | 1402 3 1404 | 1403 3 1405 | 1404 3 1406 | 1405 3 1407 | 1406 3 1408 | 1407 3 1409 | 1408 3 1410 | 1409 3 1411 | 1410 3 1412 | 1411 3 1413 | 1412 3 1414 | 1413 3 1415 | 1414 3 1416 | 1415 3 1417 | 1416 3 1418 | 1417 3 1419 | 1418 3 1420 | 1419 3 1421 | 1420 3 1422 | 1421 3 1423 | 1422 3 1424 | 1423 3 1425 | 1424 3 1426 | 1425 3 1427 | 1426 3 1428 | 1427 3 1429 | 1428 3 1430 | 1429 3 1431 | 1430 3 1432 | 1431 3 1433 | 1432 3 1434 | 1433 3 1435 | 1434 3 1436 | 1435 3 1437 | 1436 3 1438 | 1437 3 1439 | 1438 3 1440 | 1439 3 1441 | 1440 3 1442 | 1441 3 1443 | 1442 3 1444 | 1443 3 1445 | 1444 3 1446 | 1445 3 1447 | 1446 3 1448 | 1447 3 1449 | 1448 3 1450 | 1449 3 1451 | 1450 3 1452 | 1451 3 1453 | 1452 3 1454 | 1453 3 1455 | 1454 3 1456 | 1455 3 1457 | 1456 3 1458 | 1457 3 1459 | 1458 3 1460 | 1459 3 1461 | 1460 3 1462 | 1461 3 1463 | 1462 3 1464 | 1463 3 1465 | 1464 3 1466 | 1465 3 1467 | 1466 3 1468 | 1467 3 1469 | 1468 3 1470 | 1469 3 1471 | 1470 3 1472 | 1471 3 1473 | 1472 3 1474 | 1473 3 1475 | 1474 3 1476 | 1475 3 1477 | 1476 3 1478 | 1477 3 1479 | 1478 3 1480 | 1479 3 1481 | 1480 3 1482 | 1481 3 1483 | 1482 0 1484 | 1483 0 1485 | 1484 0 1486 | 1485 0 1487 | 1486 0 1488 | 1487 0 1489 | 1488 0 1490 | 1489 0 1491 | 1490 0 1492 | 1491 0 1493 | 1492 0 1494 | 1493 0 1495 | 1494 0 1496 | 1495 0 1497 | 1496 0 1498 | 1497 0 1499 | 1498 0 1500 | 1499 0 1501 | 1500 0 1502 | 1501 0 1503 | 1502 0 1504 | 1503 0 1505 | 1504 0 1506 | 1505 0 1507 | 1506 0 1508 | 1507 0 1509 | 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1581 | 1580 7 1582 | 1581 7 1583 | 1582 7 1584 | 1583 7 1585 | 1584 7 1586 | 1585 7 1587 | 1586 7 1588 | 1587 7 1589 | 1588 7 1590 | 1589 7 1591 | 1590 7 1592 | 1591 7 1593 | 1592 7 1594 | 1593 7 1595 | 1594 7 1596 | 1595 7 1597 | 1596 7 1598 | 1597 7 1599 | 1598 7 1600 | 1599 7 1601 | 1600 7 1602 | 1601 7 1603 | 1602 7 1604 | 1603 7 1605 | 1604 8 1606 | 1605 8 1607 | 1606 8 1608 | 1607 8 1609 | 1608 8 1610 | 1609 8 1611 | 1610 8 1612 | 1611 8 1613 | 1612 8 1614 | 1613 8 1615 | 1614 8 1616 | 1615 8 1617 | 1616 8 1618 | 1617 8 1619 | 1618 8 1620 | 1619 8 1621 | 1620 8 1622 | 1621 8 1623 | 1622 8 1624 | 1623 8 1625 | 1624 8 1626 | 1625 8 1627 | 1626 8 1628 | 1627 8 1629 | 1628 8 1630 | 1629 8 1631 | 1630 8 1632 | 1631 8 1633 | 1632 8 1634 | 1633 8 1635 | 1634 8 1636 | 1635 8 1637 | 1636 8 1638 | 1637 8 1639 | 1638 8 1640 | 1639 8 1641 | 1640 8 1642 | 1641 8 1643 | 1642 8 1644 | 1643 8 1645 | 1644 8 1646 | 1645 8 1647 | 1646 8 1648 | 1647 8 1649 | 1648 8 1650 | 1649 8 1651 | 1650 8 1652 | 1651 8 1653 | 1652 8 1654 | 1653 8 1655 | 1654 8 1656 | 1655 8 1657 | 1656 8 1658 | 1657 8 1659 | 1658 8 1660 | 1659 8 1661 | 1660 8 1662 | 1661 8 1663 | 1662 8 1664 | 1663 8 1665 | 1664 8 1666 | 1665 8 1667 | 1666 8 1668 | 1667 8 1669 | 1668 8 1670 | 1669 8 1671 | 1670 8 1672 | 1671 8 1673 | 1672 8 1674 | 1673 8 1675 | 1674 8 1676 | 1675 8 1677 | 1676 8 1678 | 1677 8 1679 | 1678 8 1680 | 1679 8 1681 | 1680 8 1682 | 1681 8 1683 | 1682 8 1684 | 1683 8 1685 | 1684 8 1686 | 1685 8 1687 | 1686 8 1688 | 1687 2 1689 | 1688 2 1690 | 1689 2 1691 | 1690 2 1692 | 1691 2 1693 | 1692 2 1694 | 1693 2 1695 | 1694 2 1696 | 1695 2 1697 | 1696 2 1698 | 1697 2 1699 | 1698 2 1700 | 1699 2 1701 | 1700 2 1702 | 1701 2 1703 | 1702 2 1704 | 1703 2 1705 | 1704 2 1706 | 1705 2 1707 | 1706 2 1708 | 1707 2 1709 | 1708 2 1710 | 1709 2 1711 | 1710 2 1712 | 1711 2 1713 | 1712 2 1714 | 1713 2 1715 | 1714 2 1716 | 1715 2 1717 | 1716 2 1718 | 1717 2 1719 | 1718 2 1720 | 1719 2 1721 | 1720 2 1722 | 1721 2 1723 | 1722 2 1724 | 1723 2 1725 | 1724 2 1726 | 1725 2 1727 | 1726 2 1728 | 1727 2 1729 | 1728 2 1730 | 1729 2 1731 | 1730 2 1732 | 1731 2 1733 | 1732 2 1734 | 1733 2 1735 | 1734 2 1736 | 1735 2 1737 | 1736 2 1738 | 1737 2 1739 | 1738 2 1740 | 1739 2 1741 | 1740 2 1742 | 1741 2 1743 | 1742 2 1744 | 1743 2 1745 | 1744 2 1746 | 1745 2 1747 | 1746 2 1748 | 1747 2 1749 | 1748 2 1750 | 1749 2 1751 | 1750 2 1752 | 1751 2 1753 | 1752 2 1754 | 1753 2 1755 | 1754 2 1756 | 1755 2 1757 | 1756 2 1758 | 1757 2 1759 | 1758 2 1760 | 1759 2 1761 | 1760 2 1762 | 1761 2 1763 | 1762 2 1764 | 1763 2 1765 | 1764 2 1766 | 1765 2 1767 | 1766 2 1768 | 1767 2 1769 | 1768 2 1770 | 1769 2 1771 | 1770 2 1772 | 1771 2 1773 | 1772 2 1774 | 1773 2 1775 | 1774 2 1776 | 1775 2 1777 | 1776 2 1778 | 1777 2 1779 | 1778 2 1780 | 1779 2 1781 | 1780 2 1782 | 1781 2 1783 | 1782 2 1784 | 1783 2 1785 | 1784 2 1786 | 1785 2 1787 | 1786 2 1788 | 1787 2 1789 | 1788 2 1790 | 1789 2 1791 | 1790 2 1792 | 1791 2 1793 | 1792 2 1794 | 1793 2 1795 | 1794 2 1796 | 1795 2 1797 | 1796 2 1798 | 1797 2 1799 | 1798 2 1800 | 1799 2 1801 | 1800 2 1802 | 1801 2 1803 | 1802 2 1804 | 1803 2 1805 | 1804 2 1806 | 1805 2 1807 | 1806 2 1808 | 1807 2 1809 | 1808 2 1810 | 1809 2 1811 | 1810 2 1812 | 1811 2 1813 | 1812 2 1814 | 1813 2 1815 | 1814 2 1816 | 1815 2 1817 | 1816 2 1818 | 1817 2 1819 | 1818 2 1820 | 1819 2 1821 | 1820 2 1822 | 1821 2 1823 | 1822 2 1824 | 1823 2 1825 | 1824 2 1826 | 1825 2 1827 | 1826 2 1828 | 1827 2 1829 | 1828 2 1830 | 1829 2 1831 | 1830 2 1832 | 1831 2 1833 | 1832 2 1834 | 1833 2 1835 | 1834 2 1836 | 1835 2 1837 | 1836 2 1838 | 1837 2 1839 | 1838 2 1840 | 1839 2 1841 | 1840 2 1842 | 1841 2 1843 | 1842 2 1844 | 1843 2 1845 | 1844 2 1846 | 1845 2 1847 | 1846 2 1848 | 1847 2 1849 | 1848 2 1850 | 1849 2 1851 | 1850 2 1852 | 1851 2 1853 | 1852 2 1854 | 1853 2 1855 | 1854 2 1856 | 1855 2 1857 | 1856 2 1858 | 1857 2 1859 | 1858 2 1860 | 1859 2 1861 | 1860 2 1862 | 1861 2 1863 | 1862 2 1864 | 1863 2 1865 | 1864 2 1866 | 1865 2 1867 | 1866 2 1868 | 1867 2 1869 | 1868 2 1870 | 1869 2 1871 | 1870 2 1872 | 1871 2 1873 | 1872 2 1874 | 1873 2 1875 | 1874 2 1876 | 1875 2 1877 | 1876 2 1878 | 1877 2 1879 | 1878 2 1880 | 1879 2 1881 | 1880 2 1882 | 1881 2 1883 | 1882 2 1884 | 1883 2 1885 | 1884 2 1886 | 1885 2 1887 | 1886 2 1888 | 1887 2 1889 | 1888 2 1890 | 1889 2 1891 | 1890 14 1892 | 1891 14 1893 | 1892 14 1894 | 1893 14 1895 | 1894 14 1896 | 1895 14 1897 | 1896 14 1898 | 1897 14 1899 | 1898 14 1900 | 1899 14 1901 | 1900 14 1902 | 1901 14 1903 | 1902 14 1904 | 1903 14 1905 | 1904 14 1906 | 1905 14 1907 | 1906 14 1908 | 1907 14 1909 | 1908 14 1910 | 1909 14 1911 | 1910 14 1912 | 1911 14 1913 | 1912 14 1914 | 1913 14 1915 | 1914 14 1916 | 1915 14 1917 | 1916 14 1918 | 1917 14 1919 | 1918 14 1920 | 1919 14 1921 | 1920 14 1922 | 1921 14 1923 | 1922 14 1924 | 1923 14 1925 | 1924 14 1926 | 1925 14 1927 | 1926 14 1928 | 1927 14 1929 | 1928 14 1930 | 1929 14 1931 | 1930 14 1932 | 1931 14 1933 | 1932 14 1934 | 1933 14 1935 | 1934 14 1936 | 1935 14 1937 | 1936 14 1938 | 1937 14 1939 | 1938 14 1940 | 1939 14 1941 | 1940 14 1942 | 1941 14 1943 | 1942 14 1944 | 1943 14 1945 | 1944 14 1946 | 1945 14 1947 | 1946 14 1948 | 1947 14 1949 | 1948 14 1950 | 1949 14 1951 | 1950 14 1952 | 1951 14 1953 | 1952 14 1954 | 1953 14 1955 | 1954 14 1956 | 1955 14 1957 | 1956 14 1958 | 1957 14 1959 | 1958 14 1960 | 1959 14 1961 | 1960 14 1962 | 1961 14 1963 | 1962 14 1964 | 1963 14 1965 | 1964 14 1966 | 1965 14 1967 | 1966 14 1968 | 1967 6 1969 | 1968 6 1970 | 1969 6 1971 | 1970 6 1972 | 1971 6 1973 | 1972 6 1974 | 1973 6 1975 | 1974 6 1976 | 1975 6 1977 | 1976 6 1978 | 1977 6 1979 | 1978 6 1980 | 1979 6 1981 | 1980 6 1982 | 1981 6 1983 | 1982 6 1984 | 1983 6 1985 | 1984 6 1986 | 1985 6 1987 | 1986 6 1988 | 1987 6 1989 | 1988 6 1990 | 1989 6 1991 | 1990 6 1992 | 1991 6 1993 | 1992 6 1994 | 1993 6 1995 | 1994 6 1996 | 1995 6 1997 | 1996 6 1998 | 1997 6 1999 | 1998 6 2000 | 1999 6 2001 | 2000 6 2002 | 2001 6 2003 | 2002 6 2004 | 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| 1121 5 1123 | 1122 5 1124 | 1123 5 1125 | 1124 5 1126 | 1125 5 1127 | 1126 5 1128 | 1127 5 1129 | 1128 5 1130 | 1129 5 1131 | 1130 5 1132 | 1131 5 1133 | 1132 5 1134 | 1133 5 1135 | 1134 5 1136 | 1135 5 1137 | 1136 5 1138 | 1137 5 1139 | 1138 5 1140 | 1139 5 1141 | 1140 5 1142 | 1141 5 1143 | 1142 5 1144 | 1143 5 1145 | 1144 5 1146 | 1145 5 1147 | 1146 5 1148 | 1147 5 1149 | 1148 5 1150 | 1149 5 1151 | 1150 5 1152 | 1151 5 1153 | 1152 5 1154 | 1153 5 1155 | 1154 5 1156 | 1155 5 1157 | 1156 5 1158 | 1157 5 1159 | 1158 5 1160 | 1159 5 1161 | 1160 5 1162 | 1161 5 1163 | 1162 5 1164 | 1163 5 1165 | 1164 5 1166 | 1165 5 1167 | 1166 5 1168 | 1167 5 1169 | 1168 5 1170 | 1169 5 1171 | 1170 5 1172 | 1171 5 1173 | 1172 5 1174 | 1173 5 1175 | 1174 5 1176 | 1175 5 1177 | 1176 5 1178 | 1177 5 1179 | 1178 5 1180 | 1179 5 1181 | 1180 5 1182 | 1181 5 1183 | 1182 5 1184 | 1183 5 1185 | 1184 5 1186 | 1185 5 1187 | 1186 5 1188 | 1187 5 1189 | 1188 5 1190 | 1189 5 1191 | 1190 5 1192 | 1191 5 1193 | 1192 5 1194 | 1193 5 1195 | 1194 5 1196 | 1195 5 1197 | 1196 5 1198 | 1197 5 1199 | 1198 5 1200 | 1199 5 1201 | 1200 5 1202 | 1201 5 1203 | 1202 5 1204 | 1203 5 1205 | 1204 5 1206 | 1205 5 1207 | 1206 5 1208 | 1207 11 1209 | 1208 11 1210 | 1209 11 1211 | 1210 11 1212 | 1211 11 1213 | 1212 11 1214 | 1213 11 1215 | 1214 11 1216 | 1215 11 1217 | 1216 11 1218 | 1217 11 1219 | 1218 11 1220 | 1219 11 1221 | 1220 11 1222 | 1221 11 1223 | 1222 11 1224 | 1223 11 1225 | 1224 11 1226 | 1225 11 1227 | 1226 11 1228 | 1227 11 1229 | 1228 11 1230 | 1229 11 1231 | 1230 11 1232 | 1231 11 1233 | 1232 11 1234 | 1233 11 1235 | 1234 11 1236 | 1235 11 1237 | 1236 11 1238 | 1237 11 1239 | 1238 11 1240 | 1239 11 1241 | 1240 11 1242 | 1241 11 1243 | 1242 11 1244 | 1243 11 1245 | 1244 11 1246 | 1245 11 1247 | 1246 11 1248 | 1247 11 1249 | 1248 11 1250 | 1249 11 1251 | 1250 11 1252 | 1251 11 1253 | 1252 11 1254 | 1253 11 1255 | 1254 11 1256 | 1255 11 1257 | 1256 11 1258 | 1257 11 1259 | 1258 11 1260 | 1259 11 1261 | 1260 11 1262 | 1261 11 1263 | 1262 11 1264 | 1263 11 1265 | 1264 11 1266 | 1265 11 1267 | 1266 11 1268 | 1267 11 1269 | 1268 11 1270 | 1269 11 1271 | 1270 11 1272 | 1271 11 1273 | 1272 11 1274 | 1273 11 1275 | 1274 11 1276 | 1275 11 1277 | 1276 11 1278 | 1277 11 1279 | 1278 11 1280 | 1279 11 1281 | 1280 11 1282 | 1281 11 1283 | 1282 11 1284 | 1283 11 1285 | 1284 11 1286 | 1285 11 1287 | 1286 11 1288 | 1287 11 1289 | 1288 11 1290 | 1289 11 1291 | 1290 11 1292 | 1291 11 1293 | 1292 11 1294 | 1293 11 1295 | 1294 11 1296 | 1295 11 1297 | 1296 11 1298 | 1297 11 1299 | 1298 11 1300 | 1299 11 1301 | 1300 11 1302 | 1301 11 1303 | 1302 11 1304 | 1303 11 1305 | 1304 11 1306 | 1305 11 1307 | 1306 11 1308 | 1307 11 1309 | 1308 11 1310 | 1309 11 1311 | 1310 11 1312 | 1311 11 1313 | 1312 11 1314 | 1313 11 1315 | 1314 11 1316 | 1315 11 1317 | 1316 11 1318 | 1317 11 1319 | 1318 11 1320 | 1319 11 1321 | 1320 11 1322 | 1321 11 1323 | 1322 11 1324 | 1323 11 1325 | 1324 11 1326 | 1325 11 1327 | 1326 11 1328 | 1327 11 1329 | 1328 11 1330 | 1329 11 1331 | 1330 11 1332 | 1331 11 1333 | 1332 11 1334 | 1333 11 1335 | 1334 11 1336 | 1335 11 1337 | 1336 11 1338 | 1337 11 1339 | 1338 11 1340 | 1339 11 1341 | 1340 11 1342 | 1341 11 1343 | 1342 11 1344 | 1343 11 1345 | 1344 11 1346 | 1345 11 1347 | 1346 11 1348 | 1347 11 1349 | 1348 11 1350 | 1349 11 1351 | 1350 11 1352 | 1351 11 1353 | 1352 11 1354 | 1353 11 1355 | 1354 11 1356 | 1355 11 1357 | 1356 11 1358 | 1357 11 1359 | 1358 11 1360 | 1359 11 1361 | 1360 11 1362 | 1361 11 1363 | 1362 11 1364 | 1363 11 1365 | 1364 11 1366 | 1365 11 1367 | 1366 11 1368 | 1367 11 1369 | 1368 11 1370 | 1369 11 1371 | 1370 11 1372 | 1371 11 1373 | 1372 11 1374 | 1373 11 1375 | 1374 11 1376 | 1375 11 1377 | 1376 11 1378 | 1377 11 1379 | 1378 11 1380 | 1379 11 1381 | 1380 11 1382 | 1381 11 1383 | 1382 11 1384 | 1383 11 1385 | 1384 11 1386 | 1385 11 1387 | 1386 4 1388 | 1387 4 1389 | 1388 4 1390 | 1389 4 1391 | 1390 4 1392 | 1391 4 1393 | 1392 4 1394 | 1393 4 1395 | 1394 4 1396 | 1395 4 1397 | 1396 3 1398 | 1397 3 1399 | 1398 3 1400 | 1399 3 1401 | 1400 3 1402 | 1401 3 1403 | 1402 3 1404 | 1403 3 1405 | 1404 3 1406 | 1405 3 1407 | 1406 3 1408 | 1407 3 1409 | 1408 3 1410 | 1409 3 1411 | 1410 3 1412 | 1411 3 1413 | 1412 3 1414 | 1413 3 1415 | 1414 3 1416 | 1415 3 1417 | 1416 3 1418 | 1417 3 1419 | 1418 3 1420 | 1419 3 1421 | 1420 3 1422 | 1421 3 1423 | 1422 3 1424 | 1423 3 1425 | 1424 3 1426 | 1425 3 1427 | 1426 3 1428 | 1427 3 1429 | 1428 3 1430 | 1429 3 1431 | 1430 3 1432 | 1431 3 1433 | 1432 3 1434 | 1433 3 1435 | 1434 3 1436 | 1435 3 1437 | 1436 3 1438 | 1437 3 1439 | 1438 3 1440 | 1439 3 1441 | 1440 3 1442 | 1441 3 1443 | 1442 3 1444 | 1443 3 1445 | 1444 3 1446 | 1445 3 1447 | 1446 3 1448 | 1447 3 1449 | 1448 3 1450 | 1449 3 1451 | 1450 3 1452 | 1451 3 1453 | 1452 3 1454 | 1453 3 1455 | 1454 3 1456 | 1455 3 1457 | 1456 3 1458 | 1457 3 1459 | 1458 3 1460 | 1459 3 1461 | 1460 3 1462 | 1461 3 1463 | 1462 3 1464 | 1463 3 1465 | 1464 3 1466 | 1465 3 1467 | 1466 3 1468 | 1467 3 1469 | 1468 3 1470 | 1469 3 1471 | 1470 3 1472 | 1471 3 1473 | 1472 3 1474 | 1473 3 1475 | 1474 3 1476 | 1475 3 1477 | 1476 3 1478 | 1477 3 1479 | 1478 3 1480 | 1479 3 1481 | 1480 3 1482 | 1481 3 1483 | 1482 0 1484 | 1483 0 1485 | 1484 0 1486 | 1485 0 1487 | 1486 0 1488 | 1487 0 1489 | 1488 0 1490 | 1489 0 1491 | 1490 0 1492 | 1491 0 1493 | 1492 0 1494 | 1493 0 1495 | 1494 0 1496 | 1495 0 1497 | 1496 0 1498 | 1497 0 1499 | 1498 0 1500 | 1499 0 1501 | 1500 0 1502 | 1501 0 1503 | 1502 0 1504 | 1503 0 1505 | 1504 0 1506 | 1505 0 1507 | 1506 0 1508 | 1507 0 1509 | 1508 0 1510 | 1509 0 1511 | 1510 0 1512 | 1511 0 1513 | 1512 0 1514 | 1513 0 1515 | 1514 0 1516 | 1515 0 1517 | 1516 0 1518 | 1517 0 1519 | 1518 0 1520 | 1519 0 1521 | 1520 0 1522 | 1521 0 1523 | 1522 0 1524 | 1523 0 1525 | 1524 0 1526 | 1525 0 1527 | 1526 0 1528 | 1527 0 1529 | 1528 0 1530 | 1529 0 1531 | 1530 0 1532 | 1531 0 1533 | 1532 0 1534 | 1533 0 1535 | 1534 0 1536 | 1535 0 1537 | 1536 0 1538 | 1537 0 1539 | 1538 0 1540 | 1539 0 1541 | 1540 0 1542 | 1541 0 1543 | 1542 0 1544 | 1543 0 1545 | 1544 0 1546 | 1545 0 1547 | 1546 0 1548 | 1547 0 1549 | 1548 0 1550 | 1549 0 1551 | 1550 0 1552 | 1551 7 1553 | 1552 7 1554 | 1553 7 1555 | 1554 7 1556 | 1555 7 1557 | 1556 7 1558 | 1557 7 1559 | 1558 7 1560 | 1559 7 1561 | 1560 7 1562 | 1561 7 1563 | 1562 7 1564 | 1563 7 1565 | 1564 7 1566 | 1565 7 1567 | 1566 7 1568 | 1567 7 1569 | 1568 7 1570 | 1569 7 1571 | 1570 7 1572 | 1571 7 1573 | 1572 7 1574 | 1573 7 1575 | 1574 7 1576 | 1575 7 1577 | 1576 7 1578 | 1577 7 1579 | 1578 7 1580 | 1579 7 1581 | 1580 7 1582 | 1581 7 1583 | 1582 7 1584 | 1583 7 1585 | 1584 7 1586 | 1585 7 1587 | 1586 7 1588 | 1587 7 1589 | 1588 7 1590 | 1589 7 1591 | 1590 7 1592 | 1591 7 1593 | 1592 7 1594 | 1593 7 1595 | 1594 7 1596 | 1595 7 1597 | 1596 7 1598 | 1597 7 1599 | 1598 7 1600 | 1599 7 1601 | 1600 7 1602 | 1601 7 1603 | 1602 7 1604 | 1603 7 1605 | 1604 8 1606 | 1605 8 1607 | 1606 8 1608 | 1607 8 1609 | 1608 8 1610 | 1609 8 1611 | 1610 8 1612 | 1611 8 1613 | 1612 8 1614 | 1613 8 1615 | 1614 8 1616 | 1615 8 1617 | 1616 8 1618 | 1617 8 1619 | 1618 8 1620 | 1619 8 1621 | 1620 8 1622 | 1621 8 1623 | 1622 8 1624 | 1623 8 1625 | 1624 8 1626 | 1625 8 1627 | 1626 8 1628 | 1627 8 1629 | 1628 8 1630 | 1629 8 1631 | 1630 8 1632 | 1631 8 1633 | 1632 8 1634 | 1633 8 1635 | 1634 8 1636 | 1635 8 1637 | 1636 8 1638 | 1637 8 1639 | 1638 8 1640 | 1639 8 1641 | 1640 8 1642 | 1641 8 1643 | 1642 8 1644 | 1643 8 1645 | 1644 8 1646 | 1645 8 1647 | 1646 8 1648 | 1647 8 1649 | 1648 8 1650 | 1649 8 1651 | 1650 8 1652 | 1651 8 1653 | 1652 8 1654 | 1653 8 1655 | 1654 8 1656 | 1655 8 1657 | 1656 8 1658 | 1657 8 1659 | 1658 8 1660 | 1659 8 1661 | 1660 8 1662 | 1661 8 1663 | 1662 8 1664 | 1663 8 1665 | 1664 8 1666 | 1665 8 1667 | 1666 8 1668 | 1667 8 1669 | 1668 8 1670 | 1669 8 1671 | 1670 8 1672 | 1671 8 1673 | 1672 8 1674 | 1673 8 1675 | 1674 8 1676 | 1675 8 1677 | 1676 8 1678 | 1677 8 1679 | 1678 8 1680 | 1679 8 1681 | 1680 8 1682 | 1681 8 1683 | 1682 8 1684 | 1683 8 1685 | 1684 8 1686 | 1685 8 1687 | 1686 8 1688 | 1687 2 1689 | 1688 2 1690 | 1689 2 1691 | 1690 2 1692 | 1691 2 1693 | 1692 2 1694 | 1693 2 1695 | 1694 2 1696 | 1695 2 1697 | 1696 2 1698 | 1697 2 1699 | 1698 2 1700 | 1699 2 1701 | 1700 2 1702 | 1701 2 1703 | 1702 2 1704 | 1703 2 1705 | 1704 2 1706 | 1705 2 1707 | 1706 2 1708 | 1707 2 1709 | 1708 2 1710 | 1709 2 1711 | 1710 2 1712 | 1711 2 1713 | 1712 2 1714 | 1713 2 1715 | 1714 2 1716 | 1715 2 1717 | 1716 2 1718 | 1717 2 1719 | 1718 2 1720 | 1719 2 1721 | 1720 2 1722 | 1721 2 1723 | 1722 2 1724 | 1723 2 1725 | 1724 2 1726 | 1725 2 1727 | 1726 2 1728 | 1727 2 1729 | 1728 2 1730 | 1729 2 1731 | 1730 2 1732 | 1731 2 1733 | 1732 2 1734 | 1733 2 1735 | 1734 2 1736 | 1735 2 1737 | 1736 2 1738 | 1737 2 1739 | 1738 2 1740 | 1739 2 1741 | 1740 2 1742 | 1741 2 1743 | 1742 2 1744 | 1743 2 1745 | 1744 2 1746 | 1745 2 1747 | 1746 2 1748 | 1747 2 1749 | 1748 2 1750 | 1749 2 1751 | 1750 2 1752 | 1751 2 1753 | 1752 2 1754 | 1753 2 1755 | 1754 2 1756 | 1755 2 1757 | 1756 2 1758 | 1757 2 1759 | 1758 2 1760 | 1759 2 1761 | 1760 2 1762 | 1761 2 1763 | 1762 2 1764 | 1763 2 1765 | 1764 2 1766 | 1765 2 1767 | 1766 2 1768 | 1767 2 1769 | 1768 2 1770 | 1769 2 1771 | 1770 2 1772 | 1771 2 1773 | 1772 2 1774 | 1773 2 1775 | 1774 2 1776 | 1775 2 1777 | 1776 2 1778 | 1777 2 1779 | 1778 2 1780 | 1779 2 1781 | 1780 2 1782 | 1781 2 1783 | 1782 2 1784 | 1783 2 1785 | 1784 2 1786 | 1785 2 1787 | 1786 2 1788 | 1787 2 1789 | 1788 2 1790 | 1789 2 1791 | 1790 2 1792 | 1791 2 1793 | 1792 2 1794 | 1793 2 1795 | 1794 2 1796 | 1795 2 1797 | 1796 2 1798 | 1797 2 1799 | 1798 2 1800 | 1799 2 1801 | 1800 2 1802 | 1801 2 1803 | 1802 2 1804 | 1803 2 1805 | 1804 2 1806 | 1805 2 1807 | 1806 2 1808 | 1807 2 1809 | 1808 2 1810 | 1809 2 1811 | 1810 2 1812 | 1811 2 1813 | 1812 2 1814 | 1813 2 1815 | 1814 2 1816 | 1815 2 1817 | 1816 2 1818 | 1817 2 1819 | 1818 2 1820 | 1819 2 1821 | 1820 2 1822 | 1821 2 1823 | 1822 2 1824 | 1823 2 1825 | 1824 2 1826 | 1825 2 1827 | 1826 2 1828 | 1827 2 1829 | 1828 2 1830 | 1829 2 1831 | 1830 2 1832 | 1831 2 1833 | 1832 2 1834 | 1833 2 1835 | 1834 2 1836 | 1835 2 1837 | 1836 2 1838 | 1837 2 1839 | 1838 2 1840 | 1839 2 1841 | 1840 2 1842 | 1841 2 1843 | 1842 2 1844 | 1843 2 1845 | 1844 2 1846 | 1845 2 1847 | 1846 2 1848 | 1847 2 1849 | 1848 2 1850 | 1849 2 1851 | 1850 2 1852 | 1851 2 1853 | 1852 2 1854 | 1853 2 1855 | 1854 2 1856 | 1855 2 1857 | 1856 2 1858 | 1857 2 1859 | 1858 2 1860 | 1859 2 1861 | 1860 2 1862 | 1861 2 1863 | 1862 2 1864 | 1863 2 1865 | 1864 2 1866 | 1865 2 1867 | 1866 2 1868 | 1867 2 1869 | 1868 2 1870 | 1869 2 1871 | 1870 2 1872 | 1871 2 1873 | 1872 2 1874 | 1873 2 1875 | 1874 2 1876 | 1875 2 1877 | 1876 2 1878 | 1877 2 1879 | 1878 2 1880 | 1879 2 1881 | 1880 2 1882 | 1881 2 1883 | 1882 2 1884 | 1883 2 1885 | 1884 2 1886 | 1885 2 1887 | 1886 2 1888 | 1887 2 1889 | 1888 2 1890 | 1889 2 1891 | 1890 14 1892 | 1891 14 1893 | 1892 14 1894 | 1893 14 1895 | 1894 14 1896 | 1895 14 1897 | 1896 14 1898 | 1897 14 1899 | 1898 14 1900 | 1899 14 1901 | 1900 14 1902 | 1901 14 1903 | 1902 14 1904 | 1903 14 1905 | 1904 14 1906 | 1905 14 1907 | 1906 14 1908 | 1907 14 1909 | 1908 14 1910 | 1909 14 1911 | 1910 14 1912 | 1911 14 1913 | 1912 14 1914 | 1913 14 1915 | 1914 14 1916 | 1915 14 1917 | 1916 14 1918 | 1917 14 1919 | 1918 14 1920 | 1919 14 1921 | 1920 14 1922 | 1921 14 1923 | 1922 14 1924 | 1923 14 1925 | 1924 14 1926 | 1925 14 1927 | 1926 14 1928 | 1927 14 1929 | 1928 14 1930 | 1929 14 1931 | 1930 14 1932 | 1931 14 1933 | 1932 14 1934 | 1933 14 1935 | 1934 14 1936 | 1935 14 1937 | 1936 14 1938 | 1937 14 1939 | 1938 14 1940 | 1939 14 1941 | 1940 14 1942 | 1941 14 1943 | 1942 14 1944 | 1943 14 1945 | 1944 14 1946 | 1945 14 1947 | 1946 14 1948 | 1947 14 1949 | 1948 14 1950 | 1949 14 1951 | 1950 14 1952 | 1951 14 1953 | 1952 14 1954 | 1953 14 1955 | 1954 14 1956 | 1955 14 1957 | 1956 14 1958 | 1957 14 1959 | 1958 14 1960 | 1959 14 1961 | 1960 14 1962 | 1961 14 1963 | 1962 14 1964 | 1963 14 1965 | 1964 14 1966 | 1965 14 1967 | 1966 14 1968 | 1967 6 1969 | 1968 6 1970 | 1969 6 1971 | 1970 6 1972 | 1971 6 1973 | 1972 6 1974 | 1973 6 1975 | 1974 6 1976 | 1975 6 1977 | 1976 6 1978 | 1977 6 1979 | 1978 6 1980 | 1979 6 1981 | 1980 6 1982 | 1981 6 1983 | 1982 6 1984 | 1983 6 1985 | 1984 6 1986 | 1985 6 1987 | 1986 6 1988 | 1987 6 1989 | 1988 6 1990 | 1989 6 1991 | 1990 6 1992 | 1991 6 1993 | 1992 6 1994 | 1993 6 1995 | 1994 6 1996 | 1995 6 1997 | 1996 6 1998 | 1997 6 1999 | 1998 6 2000 | 1999 6 2001 | 2000 6 2002 | 2001 6 2003 | 2002 6 2004 | 2003 6 2005 | 2004 6 2006 | 2005 6 2007 | 2006 6 2008 | 2007 6 2009 | 2008 6 2010 | 2009 6 2011 | 2010 6 2012 | 2011 6 2013 | 2012 6 2014 | 2013 6 2015 | 2014 6 2016 | 2015 6 2017 | 2016 6 2018 | 2017 6 2019 | 2018 6 2020 | 2019 6 2021 | 2020 6 2022 | 2021 6 2023 | 2022 6 2024 | 2023 6 2025 | 2024 6 2026 | 2025 6 2027 | 2026 6 2028 | 2027 6 2029 | 2028 6 2030 | 2029 6 2031 | 2030 6 2032 | 2031 6 2033 | 2032 6 2034 | 2033 6 2035 | 2034 6 2036 | 2035 6 2037 | 2036 6 2038 | 2037 6 2039 | 2038 6 2040 | 2039 6 2041 | 2040 6 2042 | 2041 6 2043 | 2042 6 2044 | 2043 6 2045 | 2044 6 2046 | 2045 6 2047 | 2046 6 2048 | 2047 6 2049 | 2048 6 2050 | 2049 6 2051 | 2050 6 2052 | 2051 6 2053 | 2052 6 2054 | 2053 6 2055 | 2054 6 2056 | 2055 6 2057 | 2056 6 2058 | 2057 6 2059 | 2058 6 2060 | 2059 6 2061 | 2060 6 2062 | 2061 6 2063 | 2062 6 2064 | 2063 6 2065 | 2064 6 2066 | 2065 6 2067 | 2066 6 2068 | 2067 6 2069 | 2068 6 2070 | 2069 6 2071 | 2070 6 2072 | 2071 6 2073 | 2072 6 2074 | 2073 6 2075 | 2074 6 2076 | 2075 6 2077 | 2076 6 2078 | 2077 6 2079 | 2078 10 2080 | 2079 10 2081 | 2080 10 2082 | 2081 10 2083 | 2082 10 2084 | 2083 10 2085 | 2084 10 2086 | 2085 10 2087 | 2086 10 2088 | 2087 10 2089 | 2088 10 2090 | 2089 10 2091 | 2090 10 2092 | 2091 10 2093 | 2092 10 2094 | 2093 10 2095 | 2094 10 2096 | 2095 10 2097 | 2096 10 2098 | 2097 10 2099 | 2098 10 2100 | 2099 10 2101 | 2100 10 2102 | 2101 10 2103 | 2102 10 2104 | 2103 10 2105 | 2104 10 2106 | 2105 10 2107 | 2106 10 2108 | 2107 10 2109 | 2108 10 2110 | 2109 10 2111 | 2110 10 2112 | 2111 10 2113 | 2112 10 2114 | 2113 10 2115 | 2114 10 2116 | 2115 10 2117 | 2116 10 2118 | 2117 10 2119 | 2118 10 2120 | 2119 10 2121 | 2120 10 2122 | 2121 10 2123 | 2122 10 2124 | 2123 10 2125 | 2124 10 2126 | 2125 10 2127 | 2126 10 2128 | 2127 10 2129 | 2128 10 2130 | 2129 10 2131 | 2130 10 2132 | 2131 10 2133 | 2132 10 2134 | 2133 10 2135 | 2134 10 2136 | 2135 10 2137 | 2136 10 2138 | 2137 10 2139 | 2138 10 2140 | 2139 10 2141 | 2140 10 2142 | 2141 10 2143 | 2142 10 2144 | 2143 10 2145 | 2144 10 2146 | 2145 10 2147 | 2146 10 2148 | 2147 10 2149 | 2148 10 2150 | 2149 10 2151 | 2150 10 2152 | 2151 10 2153 | 2152 10 2154 | 2153 10 2155 | 2154 10 2156 | 2155 10 2157 | 2156 10 2158 | 2157 10 2159 | 2158 10 2160 | 2159 10 2161 | 2160 10 2162 | 2161 10 2163 | 2162 10 2164 | 2163 10 2165 | 2164 10 2166 | 2165 10 2167 | 2166 10 2168 | 2167 10 2169 | 2168 10 2170 | 2169 10 2171 | 2170 10 2172 | 2171 10 2173 | 2172 10 2174 | 2173 10 2175 | 2174 10 2176 | 2175 10 2177 | 2176 10 2178 | 2177 10 2179 | 2178 10 2180 | 2179 10 2181 | 2180 10 2182 | 2181 10 2183 | 2182 10 2184 | 2183 10 2185 | 2184 10 2186 | 2185 10 2187 | 2186 10 2188 | 2187 10 2189 | 2188 10 2190 | 2189 10 2191 | 2190 10 2192 | 2191 10 2193 | 2192 10 2194 | 2193 10 2195 | 2194 10 2196 | 2195 10 2197 | 2196 10 2198 | 2197 10 2199 | 2198 10 2200 | 2199 10 2201 | 2200 10 2202 | 2201 10 2203 | 2202 10 2204 | 2203 10 2205 | 2204 10 2206 | 2205 10 2207 | 2206 10 2208 | 2207 10 2209 | 2208 10 2210 | 2209 10 2211 | 2210 10 2212 | 2211 10 2213 | 2212 10 2214 | 2213 10 2215 | 2214 10 2216 | 2215 10 2217 | 2216 10 2218 | 2217 10 2219 | 2218 10 2220 | 2219 10 2221 | 2220 10 2222 | 2221 10 2223 | 2222 10 2224 | 2223 10 2225 | 2224 10 2226 | 2225 10 2227 | 2226 10 2228 | 2227 10 2229 | 2228 10 2230 | 2229 10 2231 | 2230 10 2232 | 2231 10 2233 | 2232 10 2234 | 2233 10 2235 | 2234 10 2236 | 2235 10 2237 | 2236 10 2238 | 2237 10 2239 | 2238 10 2240 | 2239 10 2241 | 2240 10 2242 | 2241 10 2243 | 2242 10 2244 | 2243 10 2245 | 2244 10 2246 | 2245 10 2247 | 2246 10 2248 | 2247 10 2249 | 2248 10 2250 | 2249 10 2251 | 2250 10 2252 | 2251 10 2253 | 2252 10 2254 | 2253 10 2255 | 2254 10 2256 | 2255 10 2257 | 2256 10 2258 | 2257 10 2259 | 2258 10 2260 | 2259 10 2261 | 2260 10 2262 | 2261 10 2263 | 2262 10 2264 | 2263 10 2265 | 2264 10 2266 | 2265 10 2267 | 2266 10 2268 | 2267 10 2269 | 2268 10 2270 | 2269 10 2271 | 2270 10 2272 | 2271 10 2273 | 2272 10 2274 | 2273 10 2275 | 2274 10 2276 | 2275 10 2277 | 2276 10 2278 | 2277 10 2279 | 2278 10 2280 | 2279 10 2281 | 2280 10 2282 | 2281 10 2283 | 2282 10 2284 | 2283 10 2285 | 2284 10 2286 | 2285 10 2287 | 2286 10 2288 | 2287 10 2289 | 2288 10 2290 | 2289 10 2291 | 2290 10 2292 | 2291 10 2293 | 2292 10 2294 | 2293 10 2295 | 2294 10 2296 | 2295 10 2297 | 2296 10 2298 | 2297 10 2299 | 2298 10 2300 | 2299 10 2301 | 2300 10 2302 | 2301 10 2303 | 2302 10 2304 | 2303 10 2305 | 2304 10 2306 | 2305 10 2307 | 2306 10 2308 | 2307 10 2309 | 2308 10 2310 | 2309 10 2311 | 2310 10 2312 | 2311 10 2313 | 2312 10 2314 | 2313 10 2315 | 2314 10 2316 | 2315 10 2317 | 2316 10 2318 | 2317 15 2319 | 2318 15 2320 | 2319 15 2321 | 2320 15 2322 | 2321 15 2323 | 2322 15 2324 | 2323 15 2325 | 2324 15 2326 | 2325 15 2327 | 2326 15 2328 | 2327 15 2329 | 2328 15 2330 | 2329 15 2331 | 2330 15 2332 | 2331 15 2333 | 2332 15 2334 | 2333 15 2335 | 2334 15 2336 | 2335 15 2337 | 2336 15 2338 | 2337 15 2339 | 2338 15 2340 | 2339 15 2341 | 2340 15 2342 | 2341 15 2343 | 2342 15 2344 | 2343 15 2345 | 2344 15 2346 | 2345 15 2347 | 2346 15 2348 | 2347 15 2349 | 2348 15 2350 | 2349 15 2351 | 2350 15 2352 | 2351 15 2353 | 2352 15 2354 | 2353 15 2355 | 2354 15 2356 | 2355 15 2357 | 2356 15 2358 | 2357 15 2359 | 2358 15 2360 | 2359 15 2361 | 2360 15 2362 | 2361 15 2363 | 2362 15 2364 | 2363 15 2365 | 2364 15 2366 | 2365 15 2367 | 2366 15 2368 | 2367 15 2369 | 2368 15 2370 | 2369 15 2371 | 2370 15 2372 | 2371 15 2373 | 2372 15 2374 | 2373 15 2375 | 2374 15 2376 | 2375 15 2377 | 2376 15 2378 | 2377 15 2379 | 2378 15 2380 | 2379 15 2381 | 2380 15 2382 | 2381 15 2383 | 2382 15 2384 | 2383 15 2385 | 2384 15 2386 | 2385 15 2387 | 2386 15 2388 | 2387 15 2389 | 2388 15 2390 | 2389 15 2391 | 2390 8 2392 | 2391 8 2393 | 2392 8 2394 | 2393 8 2395 | 2394 8 2396 | 2395 8 2397 | 2396 8 2398 | 2397 8 2399 | 2398 8 2400 | 2399 8 2401 | 2400 8 2402 | 2401 8 2403 | 2402 8 2404 | 2403 8 2405 | 2404 8 2406 | -------------------------------------------------------------------------------- /examples/alias.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | import numpy as np 3 | 4 | from ge.alias import alias_sample, create_alias_table 5 | 6 | 7 | def gen_prob_dist(N): 8 | p = np.random.randint(0, 100, N) 9 | return p/np.sum(p) 10 | 11 | 12 | def simulate(N=100, k=10000,): 13 | 14 | truth = gen_prob_dist(N) 15 | 16 | area_ratio = truth 17 | accept, alias = create_alias_table(area_ratio) 18 | 19 | ans = np.zeros(N) 20 | for _ in range(k): 21 | i = alias_sample(accept, alias) 22 | ans[i] += 1 23 | return ans/np.sum(ans), truth 24 | 25 | 26 | if __name__ == "__main__": 27 | alias_result, truth = simulate() 28 | plt.bar(list(range(len(alias_result))), alias_result, label='alias_result') 29 | plt.bar(list(range(len(truth))), truth, label='truth') 30 | plt.legend() 31 | -------------------------------------------------------------------------------- /examples/deepwalk_wiki.py: -------------------------------------------------------------------------------- 1 | 2 | import numpy as np 3 | 4 | from ge.classify import read_node_label, Classifier 5 | from ge import DeepWalk 6 | from sklearn.linear_model import LogisticRegression 7 | 8 | import matplotlib.pyplot as plt 9 | import networkx as nx 10 | from sklearn.manifold import TSNE 11 | 12 | 13 | def evaluate_embeddings(embeddings): 14 | X, Y = read_node_label('../data/wiki/wiki_labels.txt') 15 | tr_frac = 0.8 16 | print("Training classifier using {:.2f}% nodes...".format( 17 | tr_frac * 100)) 18 | clf = Classifier(embeddings=embeddings, clf=LogisticRegression()) 19 | clf.split_train_evaluate(X, Y, tr_frac) 20 | 21 | 22 | def plot_embeddings(embeddings,): 23 | X, Y = read_node_label('../data/wiki/wiki_labels.txt') 24 | 25 | emb_list = [] 26 | for k in X: 27 | emb_list.append(embeddings[k]) 28 | emb_list = np.array(emb_list) 29 | 30 | model = TSNE(n_components=2) 31 | node_pos = model.fit_transform(emb_list) 32 | 33 | color_idx = {} 34 | for i in range(len(X)): 35 | color_idx.setdefault(Y[i][0], []) 36 | color_idx[Y[i][0]].append(i) 37 | 38 | for c, idx in color_idx.items(): 39 | plt.scatter(node_pos[idx, 0], node_pos[idx, 1], label=c) 40 | plt.legend() 41 | plt.show() 42 | 43 | 44 | if __name__ == "__main__": 45 | G = nx.read_edgelist('../data/wiki/Wiki_edgelist.txt', 46 | create_using=nx.DiGraph(), nodetype=None, data=[('weight', int)]) 47 | 48 | model = DeepWalk(G, walk_length=10, num_walks=80, workers=1) 49 | model.train(window_size=5, iter=3) 50 | embeddings = model.get_embeddings() 51 | 52 | evaluate_embeddings(embeddings) 53 | plot_embeddings(embeddings) 54 | -------------------------------------------------------------------------------- /examples/line_wiki.py: -------------------------------------------------------------------------------- 1 | 2 | import numpy as np 3 | 4 | from ge.classify import read_node_label, Classifier 5 | from ge import LINE 6 | from sklearn.linear_model import LogisticRegression 7 | 8 | import matplotlib.pyplot as plt 9 | import networkx as nx 10 | from sklearn.manifold import TSNE 11 | 12 | 13 | def evaluate_embeddings(embeddings): 14 | X, Y = read_node_label('../data/wiki/wiki_labels.txt') 15 | tr_frac = 0.8 16 | print("Training classifier using {:.2f}% nodes...".format( 17 | tr_frac * 100)) 18 | clf = Classifier(embeddings=embeddings, clf=LogisticRegression()) 19 | clf.split_train_evaluate(X, Y, tr_frac) 20 | 21 | 22 | def plot_embeddings(embeddings,): 23 | X, Y = read_node_label('../data/wiki/wiki_labels.txt') 24 | 25 | emb_list = [] 26 | for k in X: 27 | emb_list.append(embeddings[k]) 28 | emb_list = np.array(emb_list) 29 | 30 | model = TSNE(n_components=2) 31 | node_pos = model.fit_transform(emb_list) 32 | 33 | color_idx = {} 34 | for i in range(len(X)): 35 | color_idx.setdefault(Y[i][0], []) 36 | color_idx[Y[i][0]].append(i) 37 | 38 | for c, idx in color_idx.items(): 39 | plt.scatter(node_pos[idx, 0], node_pos[idx, 1], label=c) 40 | plt.legend() 41 | plt.show() 42 | 43 | 44 | if __name__ == "__main__": 45 | G = nx.read_edgelist('../data/wiki/Wiki_edgelist.txt', 46 | create_using=nx.DiGraph(), nodetype=None, data=[('weight', int)]) 47 | 48 | model = LINE(G, embedding_size=128, order='second') 49 | model.train(batch_size=1024, epochs=50, verbose=2) 50 | embeddings = model.get_embeddings() 51 | 52 | evaluate_embeddings(embeddings) 53 | plot_embeddings(embeddings) 54 | -------------------------------------------------------------------------------- /examples/node2vec_flight.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | 5 | from ge.classify import read_node_label,Classifier 6 | 7 | from ge import Node2Vec 8 | 9 | from sklearn.linear_model import LogisticRegression 10 | 11 | 12 | 13 | import matplotlib.pyplot as plt 14 | 15 | import networkx as nx 16 | 17 | from sklearn.manifold import TSNE 18 | 19 | 20 | 21 | def evaluate_embeddings(embeddings): 22 | 23 | X, Y = read_node_label('../data/flight/labels-brazil-airports.txt',skip_head=True) 24 | 25 | tr_frac = 0.8 26 | 27 | print("Training classifier using {:.2f}% nodes...".format( 28 | 29 | tr_frac * 100)) 30 | 31 | clf = Classifier(embeddings=embeddings, clf=LogisticRegression()) 32 | 33 | clf.split_train_evaluate(X, Y, tr_frac) 34 | 35 | 36 | 37 | 38 | 39 | def plot_embeddings(embeddings,): 40 | 41 | X, Y = read_node_label('../data/flight/labels-brazil-airports.txt',skip_head=True) 42 | 43 | 44 | 45 | emb_list = [] 46 | 47 | for k in X: 48 | 49 | emb_list.append(embeddings[k]) 50 | 51 | emb_list = np.array(emb_list) 52 | 53 | 54 | 55 | model = TSNE(n_components=2) 56 | 57 | node_pos = model.fit_transform(emb_list) 58 | 59 | 60 | 61 | color_idx = {} 62 | 63 | for i in range(len(X)): 64 | 65 | color_idx.setdefault(Y[i][0], []) 66 | 67 | color_idx[Y[i][0]].append(i) 68 | 69 | 70 | 71 | for c, idx in color_idx.items(): 72 | 73 | plt.scatter(node_pos[idx, 0], node_pos[idx, 1], label=c) # c=node_colors) 74 | 75 | plt.legend() 76 | 77 | plt.show() 78 | 79 | if __name__ == "__main__": 80 | G = nx.read_edgelist('../data/flight/brazil-airports.edgelist', create_using=nx.DiGraph(), nodetype=None, 81 | data=[('weight', int)]) 82 | 83 | model = Node2Vec(G, 10, 80, workers=1, p=0.25, q=2, use_rejection_sampling=0) 84 | model.train() 85 | embeddings = model.get_embeddings() 86 | 87 | evaluate_embeddings(embeddings) 88 | plot_embeddings(embeddings) 89 | -------------------------------------------------------------------------------- /examples/node2vec_wiki.py: -------------------------------------------------------------------------------- 1 | 2 | import numpy as np 3 | 4 | from ge.classify import read_node_label, Classifier 5 | from ge import Node2Vec 6 | from sklearn.linear_model import LogisticRegression 7 | 8 | import matplotlib.pyplot as plt 9 | import networkx as nx 10 | from sklearn.manifold import TSNE 11 | 12 | 13 | def evaluate_embeddings(embeddings): 14 | X, Y = read_node_label('../data/wiki/wiki_labels.txt') 15 | tr_frac = 0.8 16 | print("Training classifier using {:.2f}% nodes...".format( 17 | tr_frac * 100)) 18 | clf = Classifier(embeddings=embeddings, clf=LogisticRegression()) 19 | clf.split_train_evaluate(X, Y, tr_frac) 20 | 21 | 22 | def plot_embeddings(embeddings,): 23 | X, Y = read_node_label('../data/wiki/wiki_labels.txt') 24 | 25 | emb_list = [] 26 | for k in X: 27 | emb_list.append(embeddings[k]) 28 | emb_list = np.array(emb_list) 29 | 30 | model = TSNE(n_components=2) 31 | node_pos = model.fit_transform(emb_list) 32 | 33 | color_idx = {} 34 | for i in range(len(X)): 35 | color_idx.setdefault(Y[i][0], []) 36 | color_idx[Y[i][0]].append(i) 37 | 38 | for c, idx in color_idx.items(): 39 | plt.scatter(node_pos[idx, 0], node_pos[idx, 1], label=c) 40 | plt.legend() 41 | plt.show() 42 | 43 | 44 | if __name__ == "__main__": 45 | G=nx.read_edgelist('../data/wiki/Wiki_edgelist.txt', 46 | create_using = nx.DiGraph(), nodetype = None, data = [('weight', int)]) 47 | model = Node2Vec(G, walk_length=10, num_walks=80, 48 | p=0.25, q=4, workers=1, use_rejection_sampling=0) 49 | model.train(window_size = 5, iter = 3) 50 | embeddings=model.get_embeddings() 51 | 52 | evaluate_embeddings(embeddings) 53 | plot_embeddings(embeddings) 54 | -------------------------------------------------------------------------------- /examples/sdne_wiki.py: -------------------------------------------------------------------------------- 1 | 2 | import numpy as np 3 | 4 | from ge.classify import read_node_label, Classifier 5 | from ge import SDNE 6 | from sklearn.linear_model import LogisticRegression 7 | 8 | import matplotlib.pyplot as plt 9 | import networkx as nx 10 | from sklearn.manifold import TSNE 11 | 12 | 13 | def evaluate_embeddings(embeddings): 14 | X, Y = read_node_label('../data/wiki/wiki_labels.txt') 15 | tr_frac = 0.8 16 | print("Training classifier using {:.2f}% nodes...".format( 17 | tr_frac * 100)) 18 | clf = Classifier(embeddings=embeddings, clf=LogisticRegression()) 19 | clf.split_train_evaluate(X, Y, tr_frac) 20 | 21 | 22 | def plot_embeddings(embeddings,): 23 | X, Y = read_node_label('../data/wiki/wiki_labels.txt') 24 | 25 | emb_list = [] 26 | for k in X: 27 | emb_list.append(embeddings[k]) 28 | emb_list = np.array(emb_list) 29 | 30 | model = TSNE(n_components=2) 31 | node_pos = model.fit_transform(emb_list) 32 | 33 | color_idx = {} 34 | for i in range(len(X)): 35 | color_idx.setdefault(Y[i][0], []) 36 | color_idx[Y[i][0]].append(i) 37 | 38 | for c, idx in color_idx.items(): 39 | plt.scatter(node_pos[idx, 0], node_pos[idx, 1], 40 | label=c) # c=node_colors) 41 | plt.legend() 42 | plt.show() 43 | 44 | 45 | if __name__ == "__main__": 46 | G = nx.read_edgelist('../data/wiki/Wiki_edgelist.txt', 47 | create_using=nx.DiGraph(), nodetype=None, data=[('weight', int)]) 48 | 49 | model = SDNE(G, hidden_size=[256, 128],) 50 | model.train(batch_size=3000, epochs=40, verbose=2) 51 | embeddings = model.get_embeddings() 52 | 53 | evaluate_embeddings(embeddings) 54 | plot_embeddings(embeddings) 55 | -------------------------------------------------------------------------------- /examples/struc2vec_flight.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | 5 | from ge.classify import read_node_label,Classifier 6 | 7 | from ge import Struc2Vec 8 | 9 | from sklearn.linear_model import LogisticRegression 10 | 11 | 12 | 13 | import matplotlib.pyplot as plt 14 | 15 | import networkx as nx 16 | 17 | from sklearn.manifold import TSNE 18 | 19 | 20 | 21 | def evaluate_embeddings(embeddings): 22 | 23 | X, Y = read_node_label('../data/flight/labels-brazil-airports.txt',skip_head=True) 24 | 25 | tr_frac = 0.8 26 | 27 | print("Training classifier using {:.2f}% nodes...".format( 28 | 29 | tr_frac * 100)) 30 | 31 | clf = Classifier(embeddings=embeddings, clf=LogisticRegression()) 32 | 33 | clf.split_train_evaluate(X, Y, tr_frac) 34 | 35 | 36 | 37 | 38 | 39 | def plot_embeddings(embeddings,): 40 | 41 | X, Y = read_node_label('../data/flight/labels-brazil-airports.txt',skip_head=True) 42 | 43 | 44 | 45 | emb_list = [] 46 | 47 | for k in X: 48 | 49 | emb_list.append(embeddings[k]) 50 | 51 | emb_list = np.array(emb_list) 52 | 53 | 54 | 55 | model = TSNE(n_components=2) 56 | 57 | node_pos = model.fit_transform(emb_list) 58 | 59 | 60 | 61 | color_idx = {} 62 | 63 | for i in range(len(X)): 64 | 65 | color_idx.setdefault(Y[i][0], []) 66 | 67 | color_idx[Y[i][0]].append(i) 68 | 69 | 70 | 71 | for c, idx in color_idx.items(): 72 | 73 | plt.scatter(node_pos[idx, 0], node_pos[idx, 1], label=c) # c=node_colors) 74 | 75 | plt.legend() 76 | 77 | plt.show() 78 | 79 | if __name__ == "__main__": 80 | G = nx.read_edgelist('../data/flight/brazil-airports.edgelist', create_using=nx.DiGraph(), nodetype=None, 81 | data=[('weight', int)]) 82 | 83 | model = Struc2Vec(G, 10, 80, workers=4, verbose=40, ) 84 | model.train() 85 | embeddings = model.get_embeddings() 86 | 87 | evaluate_embeddings(embeddings) 88 | plot_embeddings(embeddings) -------------------------------------------------------------------------------- /ge/__init__.py: -------------------------------------------------------------------------------- 1 | from .models import * -------------------------------------------------------------------------------- /ge/alias.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | def create_alias_table(area_ratio): 5 | """ 6 | 7 | :param area_ratio: sum(area_ratio)=1 8 | :return: accept,alias 9 | """ 10 | l = len(area_ratio) 11 | accept, alias = [0] * l, [0] * l 12 | small, large = [], [] 13 | area_ratio_ = np.array(area_ratio) * l 14 | for i, prob in enumerate(area_ratio_): 15 | if prob < 1.0: 16 | small.append(i) 17 | else: 18 | large.append(i) 19 | 20 | while small and large: 21 | small_idx, large_idx = small.pop(), large.pop() 22 | accept[small_idx] = area_ratio_[small_idx] 23 | alias[small_idx] = large_idx 24 | area_ratio_[large_idx] = area_ratio_[large_idx] - \ 25 | (1 - area_ratio_[small_idx]) 26 | if area_ratio_[large_idx] < 1.0: 27 | small.append(large_idx) 28 | else: 29 | large.append(large_idx) 30 | 31 | while large: 32 | large_idx = large.pop() 33 | accept[large_idx] = 1 34 | while small: 35 | small_idx = small.pop() 36 | accept[small_idx] = 1 37 | 38 | return accept, alias 39 | 40 | 41 | def alias_sample(accept, alias): 42 | """ 43 | 44 | :param accept: 45 | :param alias: 46 | :return: sample index 47 | """ 48 | N = len(accept) 49 | i = int(np.random.random() * N) 50 | r = np.random.random() 51 | if r < accept[i]: 52 | return i 53 | else: 54 | return alias[i] 55 | -------------------------------------------------------------------------------- /ge/classify.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function 2 | 3 | import numpy 4 | from sklearn.metrics import f1_score, accuracy_score 5 | from sklearn.multiclass import OneVsRestClassifier 6 | from sklearn.preprocessing import MultiLabelBinarizer 7 | 8 | 9 | class TopKRanker(OneVsRestClassifier): 10 | def predict(self, X, top_k_list): 11 | probs = numpy.asarray(super(TopKRanker, self).predict_proba(X)) 12 | all_labels = [] 13 | for i, k in enumerate(top_k_list): 14 | probs_ = probs[i, :] 15 | labels = self.classes_[probs_.argsort()[-k:]].tolist() 16 | probs_[:] = 0 17 | probs_[labels] = 1 18 | all_labels.append(probs_) 19 | return numpy.asarray(all_labels) 20 | 21 | 22 | class Classifier(object): 23 | 24 | def __init__(self, embeddings, clf): 25 | self.embeddings = embeddings 26 | self.clf = TopKRanker(clf) 27 | self.binarizer = MultiLabelBinarizer(sparse_output=True) 28 | 29 | def train(self, X, Y, Y_all): 30 | self.binarizer.fit(Y_all) 31 | X_train = [self.embeddings[x] for x in X] 32 | Y = self.binarizer.transform(Y) 33 | self.clf.fit(X_train, Y) 34 | 35 | def evaluate(self, X, Y): 36 | top_k_list = [len(l) for l in Y] 37 | Y_ = self.predict(X, top_k_list) 38 | Y = self.binarizer.transform(Y) 39 | averages = ["micro", "macro", "samples", "weighted"] 40 | results = {} 41 | for average in averages: 42 | results[average] = f1_score(Y, Y_, average=average) 43 | results['acc'] = accuracy_score(Y, Y_) 44 | print('-------------------') 45 | print(results) 46 | return results 47 | 48 | def predict(self, X, top_k_list): 49 | X_ = numpy.asarray([self.embeddings[x] for x in X]) 50 | Y = self.clf.predict(X_, top_k_list=top_k_list) 51 | return Y 52 | 53 | def split_train_evaluate(self, X, Y, train_precent, seed=0): 54 | state = numpy.random.get_state() 55 | 56 | training_size = int(train_precent * len(X)) 57 | numpy.random.seed(seed) 58 | shuffle_indices = numpy.random.permutation(numpy.arange(len(X))) 59 | X_train = [X[shuffle_indices[i]] for i in range(training_size)] 60 | Y_train = [Y[shuffle_indices[i]] for i in range(training_size)] 61 | X_test = [X[shuffle_indices[i]] for i in range(training_size, len(X))] 62 | Y_test = [Y[shuffle_indices[i]] for i in range(training_size, len(X))] 63 | 64 | self.train(X_train, Y_train, Y) 65 | numpy.random.set_state(state) 66 | return self.evaluate(X_test, Y_test) 67 | 68 | 69 | def read_node_label(filename, skip_head=False): 70 | fin = open(filename, 'r') 71 | X = [] 72 | Y = [] 73 | while 1: 74 | if skip_head: 75 | fin.readline() 76 | l = fin.readline() 77 | if l == '': 78 | break 79 | vec = l.strip().split(' ') 80 | X.append(vec[0]) 81 | Y.append(vec[1:]) 82 | fin.close() 83 | return X, Y 84 | -------------------------------------------------------------------------------- /ge/models/__init__.py: -------------------------------------------------------------------------------- 1 | from .deepwalk import DeepWalk 2 | from .node2vec import Node2Vec 3 | from .line import LINE 4 | from .sdne import SDNE 5 | from .struc2vec import Struc2Vec 6 | 7 | 8 | __all__ = ["DeepWalk", "Node2Vec", "LINE", "SDNE", "Struc2Vec"] 9 | -------------------------------------------------------------------------------- /ge/models/deepwalk.py: -------------------------------------------------------------------------------- 1 | # -*- coding:utf-8 -*- 2 | 3 | """ 4 | 5 | 6 | 7 | Author: 8 | 9 | Weichen Shen,weichenswc@163.com 10 | 11 | 12 | 13 | Reference: 14 | 15 | [1] Perozzi B, Al-Rfou R, Skiena S. Deepwalk: Online learning of social representations[C]//Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2014: 701-710.(http://www.perozzi.net/publications/14_kdd_deepwalk.pdf) 16 | 17 | 18 | 19 | """ 20 | from gensim.models import Word2Vec 21 | 22 | from ..walker import RandomWalker 23 | 24 | 25 | class DeepWalk: 26 | def __init__(self, graph, walk_length, num_walks, workers=1): 27 | 28 | self.graph = graph 29 | self.w2v_model = None 30 | self._embeddings = {} 31 | 32 | self.walker = RandomWalker( 33 | graph, p=1, q=1, ) 34 | self.sentences = self.walker.simulate_walks( 35 | num_walks=num_walks, walk_length=walk_length, workers=workers, verbose=1) 36 | 37 | def train(self, embed_size=128, window_size=5, workers=3, iter=5, **kwargs): 38 | 39 | kwargs["sentences"] = self.sentences 40 | kwargs["min_count"] = kwargs.get("min_count", 0) 41 | kwargs["vector_size"] = embed_size 42 | kwargs["sg"] = 1 # skip gram 43 | kwargs["hs"] = 1 # deepwalk use Hierarchical Softmax 44 | kwargs["workers"] = workers 45 | kwargs["window"] = window_size 46 | kwargs["epochs"] = iter 47 | 48 | print("Learning embedding vectors...") 49 | model = Word2Vec(**kwargs) 50 | print("Learning embedding vectors done!") 51 | 52 | self.w2v_model = model 53 | return model 54 | 55 | def get_embeddings(self, ): 56 | if self.w2v_model is None: 57 | print("model not train") 58 | return {} 59 | 60 | self._embeddings = {} 61 | for word in self.graph.nodes(): 62 | self._embeddings[word] = self.w2v_model.wv[word] 63 | 64 | return self._embeddings 65 | -------------------------------------------------------------------------------- /ge/models/line.py: -------------------------------------------------------------------------------- 1 | # -*- coding:utf-8 -*- 2 | 3 | """ 4 | 5 | 6 | 7 | Author: 8 | 9 | Weichen Shen,weichenswc@163.com 10 | 11 | 12 | 13 | Reference: 14 | 15 | [1] Tang J, Qu M, Wang M, et al. Line: Large-scale information network embedding[C]//Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2015: 1067-1077.(https://arxiv.org/pdf/1503.03578.pdf) 16 | 17 | 18 | 19 | """ 20 | import math 21 | import random 22 | 23 | import numpy as np 24 | from deepctr.layers.utils import reduce_sum 25 | from tensorflow.python.keras import backend as K 26 | from tensorflow.python.keras.layers import Embedding, Input, Lambda 27 | from tensorflow.python.keras.models import Model 28 | 29 | from ..alias import create_alias_table, alias_sample 30 | from ..utils import preprocess_nxgraph 31 | 32 | 33 | def line_loss(y_true, y_pred): 34 | return -K.mean(K.log(K.sigmoid(y_true * y_pred))) 35 | 36 | 37 | def create_model(numNodes, embedding_size, order='second'): 38 | v_i = Input(shape=(1,)) 39 | v_j = Input(shape=(1,)) 40 | 41 | first_emb = Embedding(numNodes, embedding_size, name='first_emb') 42 | second_emb = Embedding(numNodes, embedding_size, name='second_emb') 43 | context_emb = Embedding(numNodes, embedding_size, name='context_emb') 44 | 45 | v_i_emb = first_emb(v_i) 46 | v_j_emb = first_emb(v_j) 47 | 48 | v_i_emb_second = second_emb(v_i) 49 | v_j_context_emb = context_emb(v_j) 50 | 51 | first = Lambda(lambda x: reduce_sum( 52 | x[0] * x[1], axis=-1, keep_dims=False), name='first_order')([v_i_emb, v_j_emb]) 53 | second = Lambda(lambda x: reduce_sum( 54 | x[0] * x[1], axis=-1, keep_dims=False), name='second_order')([v_i_emb_second, v_j_context_emb]) 55 | 56 | if order == 'first': 57 | output_list = [first] 58 | elif order == 'second': 59 | output_list = [second] 60 | else: 61 | output_list = [first, second] 62 | 63 | model = Model(inputs=[v_i, v_j], outputs=output_list) 64 | 65 | return model, {'first': first_emb, 'second': second_emb} 66 | 67 | 68 | class LINE: 69 | def __init__(self, graph, embedding_size=8, negative_ratio=5, order='second', ): 70 | """ 71 | 72 | :param graph: 73 | :param embedding_size: 74 | :param negative_ratio: 75 | :param order: 'first','second','all' 76 | """ 77 | if order not in ['first', 'second', 'all']: 78 | raise ValueError('mode must be fisrt,second,or all') 79 | 80 | self.graph = graph 81 | self.idx2node, self.node2idx = preprocess_nxgraph(graph) 82 | self.use_alias = True 83 | 84 | self.rep_size = embedding_size 85 | self.order = order 86 | 87 | self._embeddings = {} 88 | self.negative_ratio = negative_ratio 89 | self.order = order 90 | 91 | self.node_size = graph.number_of_nodes() 92 | self.edge_size = graph.number_of_edges() 93 | self.samples_per_epoch = self.edge_size * (1 + negative_ratio) 94 | 95 | self._gen_sampling_table() 96 | self.reset_model() 97 | 98 | def reset_training_config(self, batch_size, times): 99 | self.batch_size = batch_size 100 | self.steps_per_epoch = ( 101 | (self.samples_per_epoch - 1) // self.batch_size + 1) * times 102 | 103 | def reset_model(self, opt='adam'): 104 | 105 | self.model, self.embedding_dict = create_model( 106 | self.node_size, self.rep_size, self.order) 107 | self.model.compile(opt, line_loss) 108 | self.batch_it = self.batch_iter(self.node2idx) 109 | 110 | def _gen_sampling_table(self): 111 | 112 | # create sampling table for vertex 113 | power = 0.75 114 | numNodes = self.node_size 115 | node_degree = np.zeros(numNodes) # out degree 116 | node2idx = self.node2idx 117 | 118 | for edge in self.graph.edges(): 119 | node_degree[node2idx[edge[0]] 120 | ] += self.graph[edge[0]][edge[1]].get('weight', 1.0) 121 | 122 | total_sum = sum([math.pow(node_degree[i], power) 123 | for i in range(numNodes)]) 124 | norm_prob = [float(math.pow(node_degree[j], power)) / 125 | total_sum for j in range(numNodes)] 126 | 127 | self.node_accept, self.node_alias = create_alias_table(norm_prob) 128 | 129 | # create sampling table for edge 130 | numEdges = self.graph.number_of_edges() 131 | total_sum = sum([self.graph[edge[0]][edge[1]].get('weight', 1.0) 132 | for edge in self.graph.edges()]) 133 | norm_prob = [self.graph[edge[0]][edge[1]].get('weight', 1.0) * 134 | numEdges / total_sum for edge in self.graph.edges()] 135 | 136 | self.edge_accept, self.edge_alias = create_alias_table(norm_prob) 137 | 138 | def batch_iter(self, node2idx): 139 | 140 | edges = [(node2idx[x[0]], node2idx[x[1]]) for x in self.graph.edges()] 141 | 142 | data_size = self.graph.number_of_edges() 143 | shuffle_indices = np.random.permutation(np.arange(data_size)) 144 | # positive or negative mod 145 | mod = 0 146 | mod_size = 1 + self.negative_ratio 147 | h = [] 148 | t = [] 149 | sign = 0 150 | count = 0 151 | start_index = 0 152 | end_index = min(start_index + self.batch_size, data_size) 153 | while True: 154 | if mod == 0: 155 | 156 | h = [] 157 | t = [] 158 | for i in range(start_index, end_index): 159 | if random.random() >= self.edge_accept[shuffle_indices[i]]: 160 | shuffle_indices[i] = self.edge_alias[shuffle_indices[i]] 161 | cur_h = edges[shuffle_indices[i]][0] 162 | cur_t = edges[shuffle_indices[i]][1] 163 | h.append(cur_h) 164 | t.append(cur_t) 165 | sign = np.ones(len(h)) 166 | else: 167 | sign = np.ones(len(h)) * -1 168 | t = [] 169 | for i in range(len(h)): 170 | t.append(alias_sample( 171 | self.node_accept, self.node_alias)) 172 | 173 | if self.order == 'all': 174 | yield ([np.array(h), np.array(t)], [sign, sign]) 175 | else: 176 | yield ([np.array(h), np.array(t)], [sign]) 177 | mod += 1 178 | mod %= mod_size 179 | if mod == 0: 180 | start_index = end_index 181 | end_index = min(start_index + self.batch_size, data_size) 182 | 183 | if start_index >= data_size: 184 | count += 1 185 | mod = 0 186 | h = [] 187 | shuffle_indices = np.random.permutation(np.arange(data_size)) 188 | start_index = 0 189 | end_index = min(start_index + self.batch_size, data_size) 190 | 191 | def get_embeddings(self, ): 192 | self._embeddings = {} 193 | if self.order == 'first': 194 | embeddings = self.embedding_dict['first'].get_weights()[0] 195 | elif self.order == 'second': 196 | embeddings = self.embedding_dict['second'].get_weights()[0] 197 | else: 198 | embeddings = np.hstack((self.embedding_dict['first'].get_weights()[ 199 | 0], self.embedding_dict['second'].get_weights()[0])) 200 | idx2node = self.idx2node 201 | for i, embedding in enumerate(embeddings): 202 | self._embeddings[idx2node[i]] = embedding 203 | 204 | return self._embeddings 205 | 206 | def train(self, batch_size=1024, epochs=1, initial_epoch=0, verbose=1, times=1): 207 | self.reset_training_config(batch_size, times) 208 | hist = self.model.fit_generator(self.batch_it, epochs=epochs, initial_epoch=initial_epoch, 209 | steps_per_epoch=self.steps_per_epoch, 210 | verbose=verbose) 211 | 212 | return hist 213 | -------------------------------------------------------------------------------- /ge/models/node2vec.py: -------------------------------------------------------------------------------- 1 | # -*- coding:utf-8 -*- 2 | 3 | """ 4 | 5 | 6 | 7 | Author: 8 | 9 | Weichen Shen,weichenswc@163.com 10 | 11 | 12 | 13 | Reference: 14 | 15 | [1] Grover A, Leskovec J. node2vec: Scalable feature learning for networks[C]//Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2016: 855-864.(https://www.kdd.org/kdd2016/papers/files/rfp0218-groverA.pdf) 16 | 17 | 18 | 19 | """ 20 | 21 | from gensim.models import Word2Vec 22 | 23 | from ..walker import RandomWalker 24 | 25 | 26 | class Node2Vec: 27 | 28 | def __init__(self, graph, walk_length, num_walks, p=1.0, q=1.0, workers=1, use_rejection_sampling=False): 29 | 30 | self.graph = graph 31 | self._embeddings = {} 32 | self.walker = RandomWalker( 33 | graph, p=p, q=q, use_rejection_sampling=use_rejection_sampling) 34 | 35 | print("Preprocess transition probs...") 36 | self.walker.preprocess_transition_probs() 37 | 38 | self.sentences = self.walker.simulate_walks( 39 | num_walks=num_walks, walk_length=walk_length, workers=workers, verbose=1) 40 | 41 | def train(self, embed_size=128, window_size=5, workers=3, iter=5, **kwargs): 42 | kwargs["sentences"] = self.sentences 43 | kwargs["min_count"] = kwargs.get("min_count", 0) 44 | kwargs["vector_size"] = embed_size 45 | kwargs["sg"] = 1 46 | kwargs["hs"] = 0 # node2vec not use Hierarchical Softmax 47 | kwargs["workers"] = workers 48 | kwargs["window"] = window_size 49 | kwargs["epochs"] = iter 50 | 51 | print("Learning embedding vectors...") 52 | model = Word2Vec(**kwargs) 53 | print("Learning embedding vectors done!") 54 | 55 | self.w2v_model = model 56 | 57 | return model 58 | 59 | def get_embeddings(self, ): 60 | if self.w2v_model is None: 61 | print("model not train") 62 | return {} 63 | 64 | self._embeddings = {} 65 | for word in self.graph.nodes(): 66 | self._embeddings[word] = self.w2v_model.wv[word] 67 | 68 | return self._embeddings 69 | -------------------------------------------------------------------------------- /ge/models/sdne.py: -------------------------------------------------------------------------------- 1 | # -*- coding:utf-8 -*- 2 | 3 | """ 4 | 5 | 6 | 7 | Author: 8 | 9 | Weichen Shen,weichenswc@163.com 10 | 11 | 12 | 13 | Reference: 14 | 15 | [1] Wang D, Cui P, Zhu W. Structural deep network embedding[C]//Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2016: 1225-1234.(https://www.kdd.org/kdd2016/papers/files/rfp0191-wangAemb.pdf) 16 | 17 | 18 | 19 | """ 20 | import time 21 | 22 | import numpy as np 23 | import scipy.sparse as sp 24 | import tensorflow as tf 25 | from tensorflow.python.keras import backend as K 26 | from tensorflow.python.keras.callbacks import History 27 | from tensorflow.python.keras.layers import Dense, Input 28 | from tensorflow.python.keras.models import Model 29 | from tensorflow.python.keras.regularizers import l1_l2 30 | 31 | from ..utils import preprocess_nxgraph 32 | 33 | 34 | def l_2nd(beta): 35 | def loss_2nd(y_true, y_pred): 36 | b_ = np.ones_like(y_true) 37 | b_[y_true != 0] = beta 38 | x = K.square((y_true - y_pred) * b_) 39 | t = K.sum(x, axis=-1, ) 40 | return K.mean(t) 41 | 42 | return loss_2nd 43 | 44 | 45 | def l_1st(alpha): 46 | def loss_1st(y_true, y_pred): 47 | L = y_true 48 | Y = y_pred 49 | batch_size = tf.to_float(K.shape(L)[0]) 50 | return alpha * 2 * tf.linalg.trace(tf.matmul(tf.matmul(Y, L, transpose_a=True), Y)) / batch_size 51 | 52 | return loss_1st 53 | 54 | 55 | def create_model(node_size, hidden_size=[256, 128], l1=1e-5, l2=1e-4): 56 | A = Input(shape=(node_size,)) 57 | L = Input(shape=(None,)) 58 | fc = A 59 | for i in range(len(hidden_size)): 60 | if i == len(hidden_size) - 1: 61 | fc = Dense(hidden_size[i], activation='relu', 62 | kernel_regularizer=l1_l2(l1, l2), name='1st')(fc) 63 | else: 64 | fc = Dense(hidden_size[i], activation='relu', 65 | kernel_regularizer=l1_l2(l1, l2))(fc) 66 | Y = fc 67 | for i in reversed(range(len(hidden_size) - 1)): 68 | fc = Dense(hidden_size[i], activation='relu', 69 | kernel_regularizer=l1_l2(l1, l2))(fc) 70 | 71 | A_ = Dense(node_size, 'relu', name='2nd')(fc) 72 | model = Model(inputs=[A, L], outputs=[A_, Y]) 73 | emb = Model(inputs=A, outputs=Y) 74 | return model, emb 75 | 76 | 77 | class SDNE(object): 78 | def __init__(self, graph, hidden_size=[32, 16], alpha=1e-6, beta=5., nu1=1e-5, nu2=1e-4, ): 79 | 80 | self.graph = graph 81 | # self.g.remove_edges_from(self.g.selfloop_edges()) 82 | self.idx2node, self.node2idx = preprocess_nxgraph(self.graph) 83 | 84 | self.node_size = self.graph.number_of_nodes() 85 | self.hidden_size = hidden_size 86 | self.alpha = alpha 87 | self.beta = beta 88 | self.nu1 = nu1 89 | self.nu2 = nu2 90 | 91 | self.A, self.L = _create_A_L(self.graph, self.node2idx) # Adj Matrix,L Matrix 92 | self.reset_model() 93 | self.inputs = [self.A, self.L] 94 | self._embeddings = {} 95 | 96 | def reset_model(self, opt='adam'): 97 | 98 | self.model, self.emb_model = create_model(self.node_size, hidden_size=self.hidden_size, l1=self.nu1, 99 | l2=self.nu2) 100 | self.model.compile(opt, [l_2nd(self.beta), l_1st(self.alpha)]) 101 | self.get_embeddings() 102 | 103 | def train(self, batch_size=1024, epochs=1, initial_epoch=0, verbose=1): 104 | if batch_size >= self.node_size: 105 | if batch_size > self.node_size: 106 | print('batch_size({0}) > node_size({1}),set batch_size = {1}'.format( 107 | batch_size, self.node_size)) 108 | batch_size = self.node_size 109 | return self.model.fit([self.A.todense(), self.L.todense()], [self.A.todense(), self.L.todense()], 110 | batch_size=batch_size, epochs=epochs, initial_epoch=initial_epoch, verbose=verbose, 111 | shuffle=False, ) 112 | else: 113 | steps_per_epoch = (self.node_size - 1) // batch_size + 1 114 | hist = History() 115 | hist.on_train_begin() 116 | logs = {} 117 | for epoch in range(initial_epoch, epochs): 118 | start_time = time.time() 119 | losses = np.zeros(3) 120 | for i in range(steps_per_epoch): 121 | index = np.arange( 122 | i * batch_size, min((i + 1) * batch_size, self.node_size)) 123 | A_train = self.A[index, :].todense() 124 | L_mat_train = self.L[index][:, index].todense() 125 | inp = [A_train, L_mat_train] 126 | batch_losses = self.model.train_on_batch(inp, inp) 127 | losses += batch_losses 128 | losses = losses / steps_per_epoch 129 | 130 | logs['loss'] = losses[0] 131 | logs['2nd_loss'] = losses[1] 132 | logs['1st_loss'] = losses[2] 133 | epoch_time = int(time.time() - start_time) 134 | hist.on_epoch_end(epoch, logs) 135 | if verbose > 0: 136 | print('Epoch {0}/{1}'.format(epoch + 1, epochs)) 137 | print('{0}s - loss: {1: .4f} - 2nd_loss: {2: .4f} - 1st_loss: {3: .4f}'.format( 138 | epoch_time, losses[0], losses[1], losses[2])) 139 | return hist 140 | 141 | def evaluate(self, ): 142 | return self.model.evaluate(x=self.inputs, y=self.inputs, batch_size=self.node_size) 143 | 144 | def get_embeddings(self): 145 | self._embeddings = {} 146 | embeddings = self.emb_model.predict(self.A.todense(), batch_size=self.node_size) 147 | look_back = self.idx2node 148 | for i, embedding in enumerate(embeddings): 149 | self._embeddings[look_back[i]] = embedding 150 | 151 | return self._embeddings 152 | 153 | 154 | def _create_A_L(graph, node2idx): 155 | node_size = graph.number_of_nodes() 156 | A_data = [] 157 | A_row_index = [] 158 | A_col_index = [] 159 | 160 | for edge in graph.edges(): 161 | v1, v2 = edge 162 | edge_weight = graph[v1][v2].get('weight', 1) 163 | 164 | A_data.append(edge_weight) 165 | A_row_index.append(node2idx[v1]) 166 | A_col_index.append(node2idx[v2]) 167 | 168 | A = sp.csr_matrix((A_data, (A_row_index, A_col_index)), shape=(node_size, node_size)) 169 | A_ = sp.csr_matrix((A_data + A_data, (A_row_index + A_col_index, A_col_index + A_row_index)), 170 | shape=(node_size, node_size)) 171 | 172 | D = sp.diags(A_.sum(axis=1).flatten().tolist()[0]) 173 | L = D - A_ 174 | return A, L 175 | -------------------------------------------------------------------------------- /ge/models/struc2vec.py: -------------------------------------------------------------------------------- 1 | # -*- coding:utf-8 -*- 2 | 3 | """ 4 | 5 | 6 | 7 | Author: 8 | 9 | Weichen Shen,weichenswc@163.com 10 | 11 | 12 | 13 | Reference: 14 | 15 | [1] Ribeiro L F R, Saverese P H P, Figueiredo D R. struc2vec: Learning node representations from structural identity[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2017: 385-394.(https://arxiv.org/pdf/1704.03165.pdf) 16 | 17 | 18 | 19 | """ 20 | 21 | import math 22 | import os 23 | import shutil 24 | from collections import ChainMap, deque 25 | 26 | import numpy as np 27 | import pandas as pd 28 | from fastdtw import fastdtw 29 | from gensim.models import Word2Vec 30 | from joblib import Parallel, delayed 31 | 32 | from ..alias import create_alias_table 33 | from ..utils import partition_dict, preprocess_nxgraph 34 | from ..walker import BiasedWalker 35 | 36 | 37 | class Struc2Vec(): 38 | def __init__(self, graph, walk_length=10, num_walks=100, workers=1, verbose=0, stay_prob=0.3, opt1_reduce_len=True, 39 | opt2_reduce_sim_calc=True, opt3_num_layers=None, temp_path='./temp_struc2vec/', reuse=False): 40 | self.graph = graph 41 | self.idx2node, self.node2idx = preprocess_nxgraph(graph) 42 | self.idx = list(range(len(self.idx2node))) 43 | 44 | self.opt1_reduce_len = opt1_reduce_len 45 | self.opt2_reduce_sim_calc = opt2_reduce_sim_calc 46 | self.opt3_num_layers = opt3_num_layers 47 | 48 | self.resue = reuse 49 | self.temp_path = temp_path 50 | 51 | if not os.path.exists(self.temp_path): 52 | os.mkdir(self.temp_path) 53 | if not reuse: 54 | shutil.rmtree(self.temp_path) 55 | os.mkdir(self.temp_path) 56 | 57 | self.create_context_graph(self.opt3_num_layers, workers, verbose) 58 | self.prepare_biased_walk() 59 | self.walker = BiasedWalker(self.idx2node, self.temp_path) 60 | self.sentences = self.walker.simulate_walks( 61 | num_walks, walk_length, stay_prob, workers, verbose) 62 | 63 | self._embeddings = {} 64 | 65 | def create_context_graph(self, max_num_layers, workers=1, verbose=0, ): 66 | 67 | pair_distances = self._compute_structural_distance( 68 | max_num_layers, workers, verbose, ) 69 | layers_adj, layers_distances = self._get_layer_rep(pair_distances) 70 | pd.to_pickle(layers_adj, self.temp_path + 'layers_adj.pkl') 71 | 72 | layers_accept, layers_alias = self._get_transition_probs( 73 | layers_adj, layers_distances) 74 | pd.to_pickle(layers_alias, self.temp_path + 'layers_alias.pkl') 75 | pd.to_pickle(layers_accept, self.temp_path + 'layers_accept.pkl') 76 | 77 | def prepare_biased_walk(self, ): 78 | 79 | sum_weights = {} 80 | sum_edges = {} 81 | average_weight = {} 82 | gamma = {} 83 | layer = 0 84 | while (os.path.exists(self.temp_path + 'norm_weights_distance-layer-' + str(layer) + '.pkl')): 85 | probs = pd.read_pickle( 86 | self.temp_path + 'norm_weights_distance-layer-' + str(layer) + '.pkl') 87 | for v, list_weights in probs.items(): 88 | sum_weights.setdefault(layer, 0) 89 | sum_edges.setdefault(layer, 0) 90 | sum_weights[layer] += sum(list_weights) 91 | sum_edges[layer] += len(list_weights) 92 | 93 | average_weight[layer] = sum_weights[layer] / sum_edges[layer] 94 | 95 | gamma.setdefault(layer, {}) 96 | 97 | for v, list_weights in probs.items(): 98 | num_neighbours = 0 99 | for w in list_weights: 100 | if (w > average_weight[layer]): 101 | num_neighbours += 1 102 | gamma[layer][v] = num_neighbours 103 | 104 | layer += 1 105 | 106 | pd.to_pickle(average_weight, self.temp_path + 'average_weight') 107 | pd.to_pickle(gamma, self.temp_path + 'gamma.pkl') 108 | 109 | def train(self, embed_size=128, window_size=5, workers=3, iter=5): 110 | 111 | # pd.read_pickle(self.temp_path+'walks.pkl') 112 | sentences = self.sentences 113 | 114 | print("Learning representation...") 115 | model = Word2Vec(sentences, vector_size=embed_size, window=window_size, min_count=0, hs=1, sg=1, 116 | workers=workers, 117 | epochs=iter) 118 | print("Learning representation done!") 119 | self.w2v_model = model 120 | 121 | return model 122 | 123 | def get_embeddings(self, ): 124 | if self.w2v_model is None: 125 | print("model not train") 126 | return {} 127 | 128 | self._embeddings = {} 129 | for word in self.graph.nodes(): 130 | self._embeddings[word] = self.w2v_model.wv[word] 131 | 132 | return self._embeddings 133 | 134 | def _compute_ordered_degreelist(self, max_num_layers): 135 | 136 | degreeList = {} 137 | vertices = self.idx # self.g.nodes() 138 | for v in vertices: 139 | degreeList[v] = self._get_order_degreelist_node(v, max_num_layers) 140 | return degreeList 141 | 142 | def _get_order_degreelist_node(self, root, max_num_layers=None): 143 | if max_num_layers is None: 144 | max_num_layers = float('inf') 145 | 146 | ordered_degree_sequence_dict = {} 147 | visited = [False] * len(self.graph.nodes()) 148 | queue = deque() 149 | level = 0 150 | queue.append(root) 151 | visited[root] = True 152 | 153 | while (len(queue) > 0 and level <= max_num_layers): 154 | 155 | count = len(queue) 156 | if self.opt1_reduce_len: 157 | degree_list = {} 158 | else: 159 | degree_list = [] 160 | while (count > 0): 161 | 162 | top = queue.popleft() 163 | node = self.idx2node[top] 164 | degree = len(self.graph[node]) 165 | 166 | if self.opt1_reduce_len: 167 | degree_list[degree] = degree_list.get(degree, 0) + 1 168 | else: 169 | degree_list.append(degree) 170 | 171 | for nei in self.graph[node]: 172 | nei_idx = self.node2idx[nei] 173 | if not visited[nei_idx]: 174 | visited[nei_idx] = True 175 | queue.append(nei_idx) 176 | count -= 1 177 | if self.opt1_reduce_len: 178 | orderd_degree_list = [(degree, freq) 179 | for degree, freq in degree_list.items()] 180 | orderd_degree_list.sort(key=lambda x: x[0]) 181 | else: 182 | orderd_degree_list = sorted(degree_list) 183 | ordered_degree_sequence_dict[level] = orderd_degree_list 184 | level += 1 185 | 186 | return ordered_degree_sequence_dict 187 | 188 | def _compute_structural_distance(self, max_num_layers, workers=1, verbose=0, ): 189 | 190 | if os.path.exists(self.temp_path + 'structural_dist.pkl'): 191 | structural_dist = pd.read_pickle( 192 | self.temp_path + 'structural_dist.pkl') 193 | else: 194 | if self.opt1_reduce_len: 195 | dist_func = cost_max 196 | else: 197 | dist_func = cost 198 | 199 | if os.path.exists(self.temp_path + 'degreelist.pkl'): 200 | degreeList = pd.read_pickle(self.temp_path + 'degreelist.pkl') 201 | else: 202 | degreeList = self._compute_ordered_degreelist(max_num_layers) 203 | pd.to_pickle(degreeList, self.temp_path + 'degreelist.pkl') 204 | 205 | if self.opt2_reduce_sim_calc: 206 | degrees = self._create_vectors() 207 | degreeListsSelected = {} 208 | vertices = {} 209 | n_nodes = len(self.idx) 210 | for v in self.idx: # c:list of vertex 211 | nbs = get_vertices( 212 | v, len(self.graph[self.idx2node[v]]), degrees, n_nodes) 213 | vertices[v] = nbs # store nbs 214 | degreeListsSelected[v] = degreeList[v] # store dist 215 | for n in nbs: 216 | # store dist of nbs 217 | degreeListsSelected[n] = degreeList[n] 218 | else: 219 | vertices = {} 220 | for v in degreeList: 221 | vertices[v] = [vd for vd in degreeList.keys() if vd > v] 222 | 223 | results = Parallel(n_jobs=workers, verbose=verbose, )( 224 | delayed(compute_dtw_dist)(part_list, degreeList, dist_func) for part_list in 225 | partition_dict(vertices, workers)) 226 | dtw_dist = dict(ChainMap(*results)) 227 | 228 | structural_dist = convert_dtw_struc_dist(dtw_dist) 229 | pd.to_pickle(structural_dist, self.temp_path + 230 | 'structural_dist.pkl') 231 | 232 | return structural_dist 233 | 234 | def _create_vectors(self): 235 | degrees = {} # sotre v list of degree 236 | degrees_sorted = set() # store degree 237 | G = self.graph 238 | for v in self.idx: 239 | degree = len(G[self.idx2node[v]]) 240 | degrees_sorted.add(degree) 241 | if (degree not in degrees): 242 | degrees[degree] = {} 243 | degrees[degree]['vertices'] = [] 244 | degrees[degree]['vertices'].append(v) 245 | degrees_sorted = np.array(list(degrees_sorted), dtype='int') 246 | degrees_sorted = np.sort(degrees_sorted) 247 | 248 | l = len(degrees_sorted) 249 | for index, degree in enumerate(degrees_sorted): 250 | if (index > 0): 251 | degrees[degree]['before'] = degrees_sorted[index - 1] 252 | if (index < (l - 1)): 253 | degrees[degree]['after'] = degrees_sorted[index + 1] 254 | 255 | return degrees 256 | 257 | def _get_layer_rep(self, pair_distances): 258 | layer_distances = {} 259 | layer_adj = {} 260 | for v_pair, layer_dist in pair_distances.items(): 261 | for layer, distance in layer_dist.items(): 262 | vx = v_pair[0] 263 | vy = v_pair[1] 264 | 265 | layer_distances.setdefault(layer, {}) 266 | layer_distances[layer][vx, vy] = distance 267 | 268 | layer_adj.setdefault(layer, {}) 269 | layer_adj[layer].setdefault(vx, []) 270 | layer_adj[layer].setdefault(vy, []) 271 | layer_adj[layer][vx].append(vy) 272 | layer_adj[layer][vy].append(vx) 273 | 274 | return layer_adj, layer_distances 275 | 276 | def _get_transition_probs(self, layers_adj, layers_distances): 277 | layers_alias = {} 278 | layers_accept = {} 279 | 280 | for layer in layers_adj: 281 | 282 | neighbors = layers_adj[layer] 283 | layer_distances = layers_distances[layer] 284 | node_alias_dict = {} 285 | node_accept_dict = {} 286 | norm_weights = {} 287 | 288 | for v, neighbors in neighbors.items(): 289 | e_list = [] 290 | sum_w = 0.0 291 | 292 | for n in neighbors: 293 | if (v, n) in layer_distances: 294 | wd = layer_distances[v, n] 295 | else: 296 | wd = layer_distances[n, v] 297 | w = np.exp(-float(wd)) 298 | e_list.append(w) 299 | sum_w += w 300 | 301 | e_list = [x / sum_w for x in e_list] 302 | norm_weights[v] = e_list 303 | accept, alias = create_alias_table(e_list) 304 | node_alias_dict[v] = alias 305 | node_accept_dict[v] = accept 306 | 307 | pd.to_pickle( 308 | norm_weights, self.temp_path + 'norm_weights_distance-layer-' + str(layer) + '.pkl') 309 | 310 | layers_alias[layer] = node_alias_dict 311 | layers_accept[layer] = node_accept_dict 312 | 313 | return layers_accept, layers_alias 314 | 315 | 316 | def cost(a, b): 317 | ep = 0.5 318 | m = max(a, b) + ep 319 | mi = min(a, b) + ep 320 | return ((m / mi) - 1) 321 | 322 | 323 | def cost_min(a, b): 324 | ep = 0.5 325 | m = max(a[0], b[0]) + ep 326 | mi = min(a[0], b[0]) + ep 327 | return ((m / mi) - 1) * min(a[1], b[1]) 328 | 329 | 330 | def cost_max(a, b): 331 | ep = 0.5 332 | m = max(a[0], b[0]) + ep 333 | mi = min(a[0], b[0]) + ep 334 | return ((m / mi) - 1) * max(a[1], b[1]) 335 | 336 | 337 | def convert_dtw_struc_dist(distances, startLayer=1): 338 | """ 339 | 340 | :param distances: dict of dict 341 | :param startLayer: 342 | :return: 343 | """ 344 | for vertices, layers in distances.items(): 345 | keys_layers = sorted(layers.keys()) 346 | startLayer = min(len(keys_layers), startLayer) 347 | for layer in range(0, startLayer): 348 | keys_layers.pop(0) 349 | 350 | for layer in keys_layers: 351 | layers[layer] += layers[layer - 1] 352 | return distances 353 | 354 | 355 | def get_vertices(v, degree_v, degrees, n_nodes): 356 | a_vertices_selected = 2 * math.log(n_nodes, 2) 357 | vertices = [] 358 | try: 359 | c_v = 0 360 | 361 | for v2 in degrees[degree_v]['vertices']: 362 | if (v != v2): 363 | vertices.append(v2) # same degree 364 | c_v += 1 365 | if (c_v > a_vertices_selected): 366 | raise StopIteration 367 | 368 | if ('before' not in degrees[degree_v]): 369 | degree_b = -1 370 | else: 371 | degree_b = degrees[degree_v]['before'] 372 | if ('after' not in degrees[degree_v]): 373 | degree_a = -1 374 | else: 375 | degree_a = degrees[degree_v]['after'] 376 | if (degree_b == -1 and degree_a == -1): 377 | raise StopIteration # not anymore v 378 | degree_now = verifyDegrees(degrees, degree_v, degree_a, degree_b) 379 | # nearest valid degree 380 | while True: 381 | for v2 in degrees[degree_now]['vertices']: 382 | if (v != v2): 383 | vertices.append(v2) 384 | c_v += 1 385 | if (c_v > a_vertices_selected): 386 | raise StopIteration 387 | 388 | if (degree_now == degree_b): 389 | if ('before' not in degrees[degree_b]): 390 | degree_b = -1 391 | else: 392 | degree_b = degrees[degree_b]['before'] 393 | else: 394 | if ('after' not in degrees[degree_a]): 395 | degree_a = -1 396 | else: 397 | degree_a = degrees[degree_a]['after'] 398 | 399 | if (degree_b == -1 and degree_a == -1): 400 | raise StopIteration 401 | 402 | degree_now = verifyDegrees(degrees, degree_v, degree_a, degree_b) 403 | 404 | except StopIteration: 405 | return list(vertices) 406 | 407 | return list(vertices) 408 | 409 | 410 | def verifyDegrees(degrees, degree_v_root, degree_a, degree_b): 411 | if (degree_b == -1): 412 | degree_now = degree_a 413 | elif (degree_a == -1): 414 | degree_now = degree_b 415 | elif (abs(degree_b - degree_v_root) < abs(degree_a - degree_v_root)): 416 | degree_now = degree_b 417 | else: 418 | degree_now = degree_a 419 | 420 | return degree_now 421 | 422 | 423 | def compute_dtw_dist(part_list, degreeList, dist_func): 424 | dtw_dist = {} 425 | for v1, nbs in part_list: 426 | lists_v1 = degreeList[v1] # lists_v1 :orderd degree list of v1 427 | for v2 in nbs: 428 | lists_v2 = degreeList[v2] # lists_v1 :orderd degree list of v2 429 | max_layer = min(len(lists_v1), len(lists_v2)) # valid layer 430 | dtw_dist[v1, v2] = {} 431 | for layer in range(0, max_layer): 432 | dist, path = fastdtw( 433 | lists_v1[layer], lists_v2[layer], radius=1, dist=dist_func) 434 | dtw_dist[v1, v2][layer] = dist 435 | return dtw_dist 436 | -------------------------------------------------------------------------------- /ge/utils.py: -------------------------------------------------------------------------------- 1 | def preprocess_nxgraph(graph): 2 | node2idx = {} 3 | idx2node = [] 4 | node_size = 0 5 | for node in graph.nodes(): 6 | node2idx[node] = node_size 7 | idx2node.append(node) 8 | node_size += 1 9 | return idx2node, node2idx 10 | 11 | 12 | def partition_dict(vertices, workers): 13 | batch_size = (len(vertices) - 1) // workers + 1 14 | part_list = [] 15 | part = [] 16 | count = 0 17 | for v1, nbs in vertices.items(): 18 | part.append((v1, nbs)) 19 | count += 1 20 | if count % batch_size == 0: 21 | part_list.append(part) 22 | part = [] 23 | if len(part) > 0: 24 | part_list.append(part) 25 | return part_list 26 | 27 | 28 | def partition_list(vertices, workers): 29 | batch_size = (len(vertices) - 1) // workers + 1 30 | part_list = [] 31 | part = [] 32 | count = 0 33 | for v1, nbs in enumerate(vertices): 34 | part.append((v1, nbs)) 35 | count += 1 36 | if count % batch_size == 0: 37 | part_list.append(part) 38 | part = [] 39 | if len(part) > 0: 40 | part_list.append(part) 41 | return part_list 42 | 43 | 44 | def partition_num(num, workers): 45 | if num % workers == 0: 46 | return [num // workers] * workers 47 | else: 48 | return [num // workers] * workers + [num % workers] 49 | -------------------------------------------------------------------------------- /ge/walker.py: -------------------------------------------------------------------------------- 1 | import itertools 2 | import math 3 | import random 4 | 5 | import pandas as pd 6 | from joblib import Parallel, delayed 7 | 8 | from .alias import alias_sample, create_alias_table 9 | from .utils import partition_num 10 | 11 | 12 | class RandomWalker: 13 | def __init__(self, G, p=1, q=1, use_rejection_sampling=False): 14 | """ 15 | :param G: 16 | :param p: Return parameter,controls the likelihood of immediately revisiting a node in the walk. 17 | :param q: In-out parameter,allows the search to differentiate between “inward” and “outward” nodes 18 | :param use_rejection_sampling: Whether to use the rejection sampling strategy in node2vec. 19 | """ 20 | self.G = G 21 | self.p = p 22 | self.q = q 23 | self.use_rejection_sampling = use_rejection_sampling 24 | 25 | def deepwalk_walk(self, walk_length, start_node): 26 | 27 | walk = [start_node] 28 | 29 | while len(walk) < walk_length: 30 | cur = walk[-1] 31 | cur_nbrs = list(self.G.neighbors(cur)) 32 | if len(cur_nbrs) > 0: 33 | walk.append(random.choice(cur_nbrs)) 34 | else: 35 | break 36 | return walk 37 | 38 | def node2vec_walk(self, walk_length, start_node): 39 | 40 | G = self.G 41 | alias_nodes = self.alias_nodes 42 | alias_edges = self.alias_edges 43 | 44 | walk = [start_node] 45 | 46 | while len(walk) < walk_length: 47 | cur = walk[-1] 48 | cur_nbrs = list(G.neighbors(cur)) 49 | if len(cur_nbrs) > 0: 50 | if len(walk) == 1: 51 | walk.append( 52 | cur_nbrs[alias_sample(alias_nodes[cur][0], alias_nodes[cur][1])]) 53 | else: 54 | prev = walk[-2] 55 | edge = (prev, cur) 56 | next_node = cur_nbrs[alias_sample(alias_edges[edge][0], 57 | alias_edges[edge][1])] 58 | walk.append(next_node) 59 | else: 60 | break 61 | 62 | return walk 63 | 64 | def node2vec_walk2(self, walk_length, start_node): 65 | """ 66 | Reference: 67 | KnightKing: A Fast Distributed Graph Random Walk Engine 68 | http://madsys.cs.tsinghua.edu.cn/publications/SOSP19-yang.pdf 69 | """ 70 | 71 | def rejection_sample(inv_p, inv_q, nbrs_num): 72 | upper_bound = max(1.0, max(inv_p, inv_q)) 73 | lower_bound = min(1.0, min(inv_p, inv_q)) 74 | shatter = 0 75 | second_upper_bound = max(1.0, inv_q) 76 | if (inv_p > second_upper_bound): 77 | shatter = second_upper_bound / nbrs_num 78 | upper_bound = second_upper_bound + shatter 79 | return upper_bound, lower_bound, shatter 80 | 81 | G = self.G 82 | alias_nodes = self.alias_nodes 83 | inv_p = 1.0 / self.p 84 | inv_q = 1.0 / self.q 85 | walk = [start_node] 86 | while len(walk) < walk_length: 87 | cur = walk[-1] 88 | cur_nbrs = list(G.neighbors(cur)) 89 | if len(cur_nbrs) > 0: 90 | if len(walk) == 1: 91 | walk.append( 92 | cur_nbrs[alias_sample(alias_nodes[cur][0], alias_nodes[cur][1])]) 93 | else: 94 | upper_bound, lower_bound, shatter = rejection_sample( 95 | inv_p, inv_q, len(cur_nbrs)) 96 | prev = walk[-2] 97 | prev_nbrs = set(G.neighbors(prev)) 98 | while True: 99 | prob = random.random() * upper_bound 100 | if (prob + shatter >= upper_bound): 101 | next_node = prev 102 | break 103 | next_node = cur_nbrs[alias_sample( 104 | alias_nodes[cur][0], alias_nodes[cur][1])] 105 | if (prob < lower_bound): 106 | break 107 | if (prob < inv_p and next_node == prev): 108 | break 109 | _prob = 1.0 if next_node in prev_nbrs else inv_q 110 | if (prob < _prob): 111 | break 112 | walk.append(next_node) 113 | else: 114 | break 115 | return walk 116 | 117 | def simulate_walks(self, num_walks, walk_length, workers=1, verbose=0): 118 | 119 | G = self.G 120 | 121 | nodes = list(G.nodes()) 122 | 123 | results = Parallel(n_jobs=workers, verbose=verbose, )( 124 | delayed(self._simulate_walks)(nodes, num, walk_length) for num in 125 | partition_num(num_walks, workers)) 126 | 127 | walks = list(itertools.chain(*results)) 128 | 129 | return walks 130 | 131 | def _simulate_walks(self, nodes, num_walks, walk_length, ): 132 | walks = [] 133 | for _ in range(num_walks): 134 | random.shuffle(nodes) 135 | for v in nodes: 136 | if self.p == 1 and self.q == 1: 137 | walks.append(self.deepwalk_walk( 138 | walk_length=walk_length, start_node=v)) 139 | elif self.use_rejection_sampling: 140 | walks.append(self.node2vec_walk2( 141 | walk_length=walk_length, start_node=v)) 142 | else: 143 | walks.append(self.node2vec_walk( 144 | walk_length=walk_length, start_node=v)) 145 | return walks 146 | 147 | def get_alias_edge(self, t, v): 148 | """ 149 | compute unnormalized transition probability between nodes v and its neighbors give the previous visited node t. 150 | :param t: 151 | :param v: 152 | :return: 153 | """ 154 | G = self.G 155 | p = self.p 156 | q = self.q 157 | 158 | unnormalized_probs = [] 159 | for x in G.neighbors(v): 160 | weight = G[v][x].get('weight', 1.0) # w_vx 161 | if x == t: # d_tx == 0 162 | unnormalized_probs.append(weight / p) 163 | elif G.has_edge(x, t): # d_tx == 1 164 | unnormalized_probs.append(weight) 165 | else: # d_tx > 1 166 | unnormalized_probs.append(weight / q) 167 | norm_const = sum(unnormalized_probs) 168 | normalized_probs = [ 169 | float(u_prob) / norm_const for u_prob in unnormalized_probs] 170 | 171 | return create_alias_table(normalized_probs) 172 | 173 | def preprocess_transition_probs(self): 174 | """ 175 | Preprocessing of transition probabilities for guiding the random walks. 176 | """ 177 | G = self.G 178 | alias_nodes = {} 179 | for node in G.nodes(): 180 | unnormalized_probs = [G[node][nbr].get('weight', 1.0) 181 | for nbr in G.neighbors(node)] 182 | norm_const = sum(unnormalized_probs) 183 | normalized_probs = [ 184 | float(u_prob) / norm_const for u_prob in unnormalized_probs] 185 | alias_nodes[node] = create_alias_table(normalized_probs) 186 | 187 | if not self.use_rejection_sampling: 188 | alias_edges = {} 189 | 190 | for edge in G.edges(): 191 | alias_edges[edge] = self.get_alias_edge(edge[0], edge[1]) 192 | if not G.is_directed(): 193 | alias_edges[(edge[1], edge[0])] = self.get_alias_edge(edge[1], edge[0]) 194 | self.alias_edges = alias_edges 195 | 196 | self.alias_nodes = alias_nodes 197 | return 198 | 199 | 200 | class BiasedWalker: 201 | def __init__(self, idx2node, temp_path): 202 | 203 | self.idx2node = idx2node 204 | self.idx = list(range(len(self.idx2node))) 205 | self.temp_path = temp_path 206 | pass 207 | 208 | def simulate_walks(self, num_walks, walk_length, stay_prob=0.3, workers=1, verbose=0): 209 | 210 | layers_adj = pd.read_pickle(self.temp_path + 'layers_adj.pkl') 211 | layers_alias = pd.read_pickle(self.temp_path + 'layers_alias.pkl') 212 | layers_accept = pd.read_pickle(self.temp_path + 'layers_accept.pkl') 213 | gamma = pd.read_pickle(self.temp_path + 'gamma.pkl') 214 | 215 | nodes = self.idx # list(self.g.nodes()) 216 | 217 | results = Parallel(n_jobs=workers, verbose=verbose, )( 218 | delayed(self._simulate_walks)(nodes, num, walk_length, stay_prob, layers_adj, layers_accept, layers_alias, 219 | gamma) for num in 220 | partition_num(num_walks, workers)) 221 | 222 | walks = list(itertools.chain(*results)) 223 | return walks 224 | 225 | def _simulate_walks(self, nodes, num_walks, walk_length, stay_prob, layers_adj, layers_accept, layers_alias, gamma): 226 | walks = [] 227 | for _ in range(num_walks): 228 | random.shuffle(nodes) 229 | for v in nodes: 230 | walks.append(self._exec_random_walk(layers_adj, layers_accept, layers_alias, 231 | v, walk_length, gamma, stay_prob)) 232 | return walks 233 | 234 | def _exec_random_walk(self, graphs, layers_accept, layers_alias, v, walk_length, gamma, stay_prob=0.3): 235 | initialLayer = 0 236 | layer = initialLayer 237 | 238 | path = [] 239 | path.append(self.idx2node[v]) 240 | 241 | while len(path) < walk_length: 242 | r = random.random() 243 | if (r < stay_prob): # same layer 244 | v = chooseNeighbor(v, graphs, layers_alias, 245 | layers_accept, layer) 246 | path.append(self.idx2node[v]) 247 | else: # different layer 248 | r = random.random() 249 | try: 250 | x = math.log(gamma[layer][v] + math.e) 251 | p_moveup = (x / (x + 1)) 252 | except: 253 | print(layer, v) 254 | raise ValueError() 255 | 256 | if (r > p_moveup): 257 | if (layer > initialLayer): 258 | layer = layer - 1 259 | else: 260 | if ((layer + 1) in graphs and v in graphs[layer + 1]): 261 | layer = layer + 1 262 | 263 | return path 264 | 265 | 266 | def chooseNeighbor(v, graphs, layers_alias, layers_accept, layer): 267 | v_list = graphs[layer][v] 268 | 269 | idx = alias_sample(layers_accept[layer][v], layers_alias[layer][v]) 270 | v = v_list[idx] 271 | 272 | return v 273 | -------------------------------------------------------------------------------- /pics/code.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shenweichen/GraphEmbedding/c8efad063c6a1162cb545385b3e18559b2e46df9/pics/code.png -------------------------------------------------------------------------------- /pics/deepctrbot.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shenweichen/GraphEmbedding/c8efad063c6a1162cb545385b3e18559b2e46df9/pics/deepctrbot.png -------------------------------------------------------------------------------- /pics/edge_list.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shenweichen/GraphEmbedding/c8efad063c6a1162cb545385b3e18559b2e46df9/pics/edge_list.png -------------------------------------------------------------------------------- /pics/weichennote.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shenweichen/GraphEmbedding/c8efad063c6a1162cb545385b3e18559b2e46df9/pics/weichennote.png -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | import setuptools 2 | 3 | 4 | with open("README.md", "r") as fh: 5 | 6 | long_description = fh.read() 7 | 8 | 9 | REQUIRED_PACKAGES = [ 10 | # 'tensorflow>=1.4.0', 11 | 'gensim>=4.0.0', 12 | 'networkx', 13 | 'joblib', 14 | 'fastdtw', 15 | 'tqdm', 16 | 'numpy', 17 | 'scikit-learn', 18 | 'pandas', 19 | 'matplotlib', 20 | 'deepctr' 21 | ] 22 | 23 | 24 | setuptools.setup( 25 | 26 | name="ge", 27 | 28 | version="0.0.0", 29 | 30 | author="Weichen Shen", 31 | 32 | author_email="weichenswc@163.com", 33 | 34 | url="https://github.com/shenweichen/GraphEmbedding", 35 | 36 | packages=setuptools.find_packages(exclude=[]), 37 | 38 | python_requires='>=3.5', # 3.4.6 39 | 40 | install_requires=REQUIRED_PACKAGES, 41 | 42 | extras_require={ 43 | 44 | "cpu": ['tensorflow>=1.4.0,!=1.7.*,!=1.8.*'], 45 | 46 | "gpu": ['tensorflow-gpu>=1.4.0,!=1.7.*,!=1.8.*'], 47 | 48 | }, 49 | 50 | entry_points={ 51 | 52 | }, 53 | license="MIT license", 54 | 55 | 56 | ) 57 | -------------------------------------------------------------------------------- /tests/Wiki_edgelist.txt: -------------------------------------------------------------------------------- 1 | 0 1 2 | 0 2 3 | 0 3 4 | 1 2 5 | 2 3 -------------------------------------------------------------------------------- /tests/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shenweichen/GraphEmbedding/c8efad063c6a1162cb545385b3e18559b2e46df9/tests/__init__.py -------------------------------------------------------------------------------- /tests/deepwalk_test.py: -------------------------------------------------------------------------------- 1 | import networkx as nx 2 | 3 | from ge import DeepWalk 4 | 5 | 6 | def test_DeepWalk(): 7 | G = nx.read_edgelist('./tests/Wiki_edgelist.txt', 8 | create_using=nx.DiGraph(), nodetype=None, data=[('weight', int)]) 9 | 10 | model = DeepWalk(G, walk_length=3, num_walks=2, workers=1) 11 | model.train(window_size=3, iter=1) 12 | embeddings = model.get_embeddings() 13 | 14 | 15 | if __name__ == "__main__": 16 | pass 17 | -------------------------------------------------------------------------------- /tests/line_test.py: -------------------------------------------------------------------------------- 1 | import networkx as nx 2 | 3 | from ge import LINE 4 | 5 | 6 | def test_LINE(): 7 | G = nx.read_edgelist('./tests/Wiki_edgelist.txt', 8 | create_using=nx.DiGraph(), nodetype=None, data=[('weight', int)]) 9 | 10 | model = LINE(G, embedding_size=2, order='second') 11 | model.train(batch_size=2, epochs=1, verbose=2) 12 | embeddings = model.get_embeddings() 13 | 14 | 15 | if __name__ == "__main__": 16 | pass 17 | -------------------------------------------------------------------------------- /tests/node2vec_test.py: -------------------------------------------------------------------------------- 1 | import networkx as nx 2 | import pytest 3 | 4 | from ge import Node2Vec 5 | 6 | 7 | @pytest.mark.parametrize( 8 | 'use_rejection_sampling', 9 | [True, False 10 | ] 11 | ) 12 | def test_Node2Vec(use_rejection_sampling): 13 | G = nx.read_edgelist('./tests/Wiki_edgelist.txt', 14 | create_using=nx.DiGraph(), nodetype=None, data=[('weight', int)]) 15 | model = Node2Vec(G, walk_length=10, num_walks=80, 16 | p=0.25, q=4, workers=1, use_rejection_sampling=use_rejection_sampling) 17 | model.train(window_size=5, iter=3) 18 | embeddings = model.get_embeddings() 19 | 20 | 21 | if __name__ == "__main__": 22 | pass 23 | -------------------------------------------------------------------------------- /tests/sdne_test.py: -------------------------------------------------------------------------------- 1 | import networkx as nx 2 | import tensorflow as tf 3 | 4 | from ge import SDNE 5 | 6 | 7 | def test_SDNE(): 8 | if tf.__version__ >= '1.15.0': 9 | return #todo 10 | G = nx.read_edgelist('./tests/Wiki_edgelist.txt', 11 | create_using=nx.DiGraph(), nodetype=None, data=[('weight', int)]) 12 | 13 | model = SDNE(G, hidden_size=[8, 4], ) 14 | model.train(batch_size=2, epochs=1, verbose=2) 15 | embeddings = model.get_embeddings() 16 | 17 | 18 | if __name__ == "__main__": 19 | pass 20 | -------------------------------------------------------------------------------- /tests/struct2vec_test.py: -------------------------------------------------------------------------------- 1 | import networkx as nx 2 | 3 | from ge import Struc2Vec 4 | 5 | 6 | def test_Struc2Vec(): 7 | G = nx.read_edgelist('./tests/Wiki_edgelist.txt', create_using=nx.DiGraph(), nodetype=None, 8 | data=[('weight', int)]) 9 | 10 | model = Struc2Vec(G, 3, 1, workers=1, verbose=40, ) 11 | model.train() 12 | embeddings = model.get_embeddings() 13 | 14 | 15 | if __name__ == "__main__": 16 | pass 17 | --------------------------------------------------------------------------------