Updating conversion, creating readmes
This commit is contained in:
Jonas Zeunert
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[38;5;12m [39m[38;2;255;187;0m[1m[4mawesome-network-embedding[0m
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[38;5;12m [39m[38;2;255;187;0m[1m[4mawesome-network-embedding[0m
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[38;5;14m[1m![0m[38;5;12mAwesome[39m[38;5;14m[1m (https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)[0m[38;5;12m (https://github.com/sindresorhus/awesome)[39m
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[38;5;14m[1m![0m[38;5;12mPRs Welcome[39m[38;5;14m[1m (https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)[0m[38;5;12m (http://makeapullrequest.com)[39m
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[38;5;14m[1m![0m[38;5;12mGitter chat for developers at https://gitter.im/dmlc/xgboost[39m[38;5;14m[1m (https://badges.gitter.im/Join%20Chat.svg)[0m[38;5;12m (https://gitter.im/awesome-network-embedding/Lobby)[39m
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[38;5;12m [39m[38;2;255;187;0m[1m[4mPaper References with the implementation(s)[0m
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[38;5;12m [39m[38;2;255;187;0m[1m[4mPaper References with the implementation(s)[0m
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[38;5;12m- [39m[38;5;14m[1mGraphGym[0m
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[38;5;12m - A platform for designing and evaluating Graph Neural Networks (GNN), NeurIPS 2020[39m
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[38;5;12m - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://proceedings.neurips.cc/paper/2020/file/c5c3d4fe6b2cc463c7d7ecba17cc9de7-Paper.pdf)[39m
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[38;5;12m - [39m[38;5;12msource[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/TimDettmers/ConvE)[39m
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[38;5;12m- [39m[38;5;14m[1mnode2vec[0m
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[38;5;12m - [39m[38;5;14m[1mnode2vec: Scalable Feature Learning for Networks[0m[38;5;12m (http://dl.acm.org/citation.cfm?id=2939672.2939754), KDD'16[39m
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[38;5;12m [39m[38;5;12m-[39m[38;5;12m [39m[38;5;12marxiv[39m[38;5;14m[1m [0m[38;5;12m [39m[38;5;12m(https://arxiv.org/abs/1607.00653)[39m[38;5;12m [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m [39m[38;5;12m(https://github.com/aditya-grover/node2vec)[39m[38;5;12m [39m[38;5;12mPython-2[39m[38;5;14m[1m [0m[38;5;12m [39m[38;5;12m(https://github.com/apple2373/node2vec)[39m[38;5;12m [39m[38;5;12mPython-3[39m[38;5;14m[1m [0m[38;5;12m [39m[38;5;12m(https://github.com/eliorc/node2vec)[39m[38;5;12m [39m[38;5;12mC++[39m[38;5;14m[1m [0m[38;5;12m [39m
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[38;5;12m(https://github.com/xgfs/node2vec-c)[39m[38;5;12m [39m
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[38;5;12m [39m[38;5;12m-[39m[38;5;12m [39m[38;5;12marxiv[39m[38;5;14m[1m [0m[38;5;12m [39m[38;5;12m(https://arxiv.org/abs/1607.00653)[39m[38;5;12m [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m [39m[38;5;12m(https://github.com/aditya-grover/node2vec)[39m[38;5;12m [39m[38;5;12mPython-2[39m[38;5;14m[1m [0m[38;5;12m [39m[38;5;12m(https://github.com/apple2373/node2vec)[39m[38;5;12m [39m[38;5;12mPython-3[39m[38;5;14m[1m [0m[38;5;12m [39m[38;5;12m(https://github.com/eliorc/node2vec)[39m[38;5;12m [39m[38;5;12mC++[39m[38;5;14m[1m [0m[38;5;12m [39m[38;5;12m(https://github.com/xgfs/node2vec-c)[39m
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[38;5;12m- [39m[38;5;14m[1mDNGR[0m
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[38;5;12m - [39m[38;5;14m[1mDeep Neural Networks for Learning Graph Representations[0m[38;5;12m (http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12423), AAAI'16[39m
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[38;5;12m - [39m[38;5;12mMatlab[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/ShelsonCao/DNGR) [39m[38;5;12mPython Keras[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/MdAsifKhan/DNGR-Keras)[39m
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[38;5;12m - Deep Network Embedding for Graph Representation Learning in Signed Networks[39m
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[38;5;12m - [39m[38;5;12mpaper[39m[38;5;14m[1m [0m[38;5;12m (https://ieeexplore.ieee.org/document/8486671) [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/shenxiaocam/Deep-network-embedding-for-graph-representation-learning-in-signed-networks)[39m
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[38;5;12m [39m[38;2;255;187;0m[1m[4mPaper References[0m
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[38;5;12m [39m[38;2;255;187;0m[1m[4mPaper References[0m
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[38;5;14m[1mA Comprehensive Survey on Graph Neural Networks[0m[38;5;12m (https://arxiv.org/abs/1901.00596), arxiv'19[39m
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[38;5;14m[1mComprehend DeepWalk as Matrix Factorization[0m[38;5;12m (https://arxiv.org/abs/1501.00358), arxiv[39m
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[38;5;12m [39m[38;2;255;187;0m[1m[4mConference & Workshop[0m
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[38;5;12m [39m[38;2;255;187;0m[1m[4mConference & Workshop[0m
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[38;5;14m[1mGraph Neural Networks for Natural Language Processing[0m[38;5;12m (https://github.com/svjan5/GNNs-for-NLP), [39m[38;5;14m[1mEMNLP'19[0m
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[38;5;14m[1mWWW-18 Tutorial Representation Learning on Networks[0m[38;5;12m (http://snap.stanford.edu/proj/embeddings-www/), [39m[38;5;14m[1mWWW'18[0m
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[38;5;12m [39m[38;2;255;187;0m[1m[4mRelated List[0m
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[38;5;12m [39m[38;2;255;187;0m[1m[4mRelated List[0m
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[38;5;14m[1mawesome-graph-classification[0m[38;5;12m (https://github.com/benedekrozemberczki/awesome-graph-classification)[39m
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[38;5;14m[1mawesome-graph-classification[0m[38;5;12m (https://github.com/benedekrozemberczki/awesome-graph-classification)[39m
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[38;5;12m [39m[38;2;255;187;0m[1m[4mRelated Project[0m
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[38;5;12m [39m[38;2;255;187;0m[1m[4mRelated Project[0m
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[38;5;14m[1mStanford Network Analysis Project[0m[38;5;12m [39m[38;5;14m[1mwebsite[0m[38;5;12m (http://snap.stanford.edu/)[39m
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