[38;5;12m                                                                                                         [39m[38;2;255;187;0m[1m[4mawesome-network-embedding[0m
[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
[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
[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

[38;5;12mAlso called network representation learning, graph embedding, knowledge embedding, etc.[39m

[38;5;12mThe task is to learn the representations of the vertices from a given network.[39m

[38;5;12mCALL FOR HELP: I'm planning to re-organize the papers with clear classification index in the near future. Please feel free to submit a commit if you find any interesting related work:)[39m



[38;5;12m                                                                                                [39m[38;2;255;187;0m[1m[4mPaper References with the implementation(s)[0m
[38;5;12m- [39m[38;5;14m[1mGraphGym[0m
[38;5;12m  - A platform for designing and evaluating Graph Neural Networks (GNN), NeurIPS 2020[39m
[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
[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/snap-stanford/graphgym)[39m
[38;5;12m- [39m[38;5;14m[1mFEATHER[0m
[38;5;12m  - Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models, CIKM 2020[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2005.07959)[39m
[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/FEATHER)[39m
[38;5;12m  - [39m[38;5;12mPython KarateClub[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/karateclub)[39m
[38;5;12m- [39m[38;5;14m[1mHeGAN[0m
[38;5;12m  - Adversarial Learning on Heterogeneous Information Networks, KDD 2019[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://fangyuan1st.github.io/paper/KDD19_HeGAN.pdf)[39m
[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/librahu/HeGAN)[39m
[38;5;12m- [39m[38;5;14m[1mNetMF[0m
[38;5;12m  - Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and Node2Vec, WSDM 2018[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://keg.cs.tsinghua.edu.cn/jietang/publications/WSDM18-Qiu-et-al-NetMF-network-embedding.pdf)[39m
[38;5;12m  - [39m[38;5;12mPython KarateClub[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/karateclub)[39m
[38;5;12m- [39m[38;5;14m[1mGL2Vec[0m
[38;5;12m  - GL2vec: Graph Embedding Enriched by Line Graphs with Edge Features, ICONIP 2019[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://link.springer.com/chapter/10.1007/978-3-030-36718-3_1)[39m
[38;5;12m  - [39m[38;5;12mPython KarateClub[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/karateclub)[39m
[38;5;12m- [39m[38;5;14m[1mNNSED[0m
[38;5;12m  - A Non-negative Symmetric Encoder-Decoder Approach for Community Detection, CIKM 2017[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.bigdatalab.ac.cn/~shenhuawei/publications/2017/cikm-sun.pdf)[39m
[38;5;12m  - [39m[38;5;12mPython KarateClub[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/karateclub) [39m
[38;5;12m- [39m[38;5;14m[1mSymmNMF[0m
[38;5;12m  - Symmetric Nonnegative Matrix Factorization for Graph Clustering, SDM 2012[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.cc.gatech.edu/~hpark/papers/DaDingParkSDM12.pdf)[39m
[38;5;12m  - [39m[38;5;12mPython KarateClub[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/karateclub)[39m
[38;5;12m- [39m[38;5;14m[1mRECT[0m
[38;5;12m  - Network Embedding with Completely-Imbalanced Labels, TKDE 2020[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://zhengwang100.github.io/pdf/TKDE20_wzheng.pdf)[39m
[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/zhengwang100/RECT) [39m
[38;5;12m- [39m[38;5;14m[1mGEMSEC[0m
[38;5;12m  - GEMSEC: Graph Embedding with Self Clustering, ASONAM 2019[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1802.03997)[39m
[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/GEMSEC) [39m
[38;5;12m- [39m[38;5;14m[1mAmpliGraph[0m
[38;5;12m  - Library for learning knowledge graph embeddings with TensorFlow [39m
[38;5;12m  - [39m[38;5;12mProject[39m[38;5;14m[1m [0m[38;5;12m (http://docs.ampligraph.org)[39m
[38;5;12m  - [39m[38;5;12mcode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/Accenture/AmpliGraph)[39m
[38;5;12m- [39m[38;5;14m[1mjodie[0m
[38;5;12m  - Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks, KDD'19[39m
[38;5;12m  - [39m[38;5;12mProject[39m[38;5;14m[1m [0m[38;5;12m (http://snap.stanford.edu/jodie/)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/srijankr/jodie/)[39m
[38;5;12m- [39m[38;5;14m[1mPyTorch-BigGraph[0m
[38;5;12m  - Pytorch-BigGraph - a distributed system for learning graph embeddings for large graphs, SysML'19[39m
[38;5;12m  - [39m[38;5;12mgithub[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/facebookresearch/PyTorch-BigGraph)[39m
[38;5;12m- [39m[38;5;14m[1mATP[0m
[38;5;12m  - ATP: Directed Graph Embedding with Asymmetric Transitivity Preservation, AAAI'19[39m
[38;5;12m  - [39m[38;5;12mpaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1811.00839)[39m
[38;5;12m  - [39m[38;5;12mcode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/zhenv5/atp)[39m
[38;5;12m- [39m[38;5;14m[1mMUSAE[0m
[38;5;12m  - Multi-scale Attributed Node Embedding, ArXiv 2019[39m
[38;5;12m  - [39m[38;5;12mpaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1909.13021)[39m
[38;5;12m  - [39m[38;5;12mPython KarateClub[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/karateclub)[39m
[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/MUSAE)[39m
[38;5;12m- [39m[38;5;14m[1mSEAL-CI[0m
[38;5;12m  - Semi-Supervised Graph Classification: A Hierarchical Graph Perspective, WWW'19[39m
[38;5;12m  - [39m[38;5;12mpaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1904.05003.pdf)[39m
[38;5;12m  - [39m[38;5;12mPython PyTorch[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/SEAL-CI)[39m
[38;5;12m- [39m[38;5;14m[1mN-GCN and MixHop[0m
[38;5;12m  - A Higher-Order Graph Convolutional Layer, NIPS'18 (workshop)[39m
[38;5;12m  - [39m[38;5;12mpaper[39m[38;5;14m[1m [0m[38;5;12m (http://sami.haija.org/papers/high-order-gc-layer.pdf)[39m
[38;5;12m  - [39m[38;5;12mPython PyTorch[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/MixHop-and-N-GCN)[39m
[38;5;12m- [39m[38;5;14m[1mCapsGNN[0m
[38;5;12m  - Capsule Graph Neural Network, ICLR'19[39m
[38;5;12m  - [39m[38;5;12mpaper[39m[38;5;14m[1m [0m[38;5;12m (https://openreview.net/forum?id=Byl8BnRcYm)[39m
[38;5;12m  - [39m[38;5;12mPython PyTorch[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/CapsGNN)[39m
[38;5;12m- [39m[38;5;14m[1mSplitter[0m
[38;5;12m  - Splitter: Learning Node Representations that Capture Multiple Social Contexts, WWW'19[39m
[38;5;12m  - [39m[38;5;12mpaper[39m[38;5;14m[1m [0m[38;5;12m (http://epasto.org/papers/www2019splitter.pdf)[39m
[38;5;12m  - [39m[38;5;12mPython PyTorch[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/Splitter)[39m
[38;5;12m- [39m[38;5;14m[1mREGAL[0m
[38;5;12m  - REGAL: Representation Learning-based Graph Alignment. International Conference on Information and Knowledge Management, CIKM'18[39m
[38;5;12m  - [39m[38;5;12marxiv[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1802.06257.pdf)[39m
[38;5;12m  - [39m[38;5;12mpaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/citation.cfm?id=3271788)[39m
[38;5;12m  - [39m[38;5;12mcode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/GemsLab/REGAL)[39m
[38;5;12m- [39m[38;5;14m[1mPyTorch Geometric[0m
[38;5;12m  - Fast Graph Representation Learning With PyTorch Geometric[39m
[38;5;12m  - [39m[38;5;12mpaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1903.02428.pdf)[39m
[38;5;12m  - [39m[38;5;12mPython PyTorch[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/rusty1s/pytorch_geometric)[39m
[38;5;12m- [39m[38;5;14m[1mTuckER[0m
[38;5;12m  - Tensor Factorization for Knowledge Graph Completion, Arxiv'19[39m
[38;5;12m  - [39m[38;5;12mpaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1901.09590.pdf)[39m
[38;5;12m  - [39m[38;5;12mPython PyTorch[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/ibalazevic/TuckER)[39m
[38;5;12m- [39m[38;5;14m[1mHypER[0m
[38;5;12m  - Hypernetwork Knowledge Graph Embeddings, Arxiv'18[39m
[38;5;12m  - [39m[38;5;12mpaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1808.07018.pdf)[39m
[38;5;12m  - [39m[38;5;12mPython PyTorch[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/ibalazevic/HypER)[39m
[38;5;12m- [39m[38;5;14m[1mGWNN[0m
[38;5;12m  - Graph Wavelet Neural Network, ICLR'19[39m
[38;5;12m  - [39m[38;5;12mpaper[39m[38;5;14m[1m [0m[38;5;12m (https://openreview.net/forum?id=H1ewdiR5tQ)[39m
[38;5;12m  - [39m[38;5;12mPython PyTorch[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/GraphWaveletNeuralNetwork)[39m
[38;5;12m  - [39m[38;5;12mPython TensorFlow[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/Eilene/GWNN)[39m
[38;5;12m- [39m[38;5;14m[1mAPPNP[0m
[38;5;12m  - Combining Neural Networks with Personalized PageRank for Classification on Graphs, ICLR'19[39m
[38;5;12m  - [39m[38;5;12mpaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1810.05997)[39m
[38;5;12m  - [39m[38;5;12mPython PyTorch[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/APPNP)[39m
[38;5;12m  - [39m[38;5;12mPython TensorFlow[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/klicperajo/ppnp)[39m
[38;5;12m- [39m[38;5;14m[1mrole2vec[0m
[38;5;12m  - Learning Role-based Graph Embeddings, IJCAI'18[39m
[38;5;12m  - [39m[38;5;12mpaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1802.02896.pdf)[39m
[38;5;12m  - [39m[38;5;12mPython KarateClub[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/karateclub)[39m
[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/role2vec)[39m
[38;5;12m- [39m[38;5;14m[1mAttentionWalk[0m
[38;5;12m  - Watch Your Step: Learning Node Embeddings via Graph Attention, NIPS'18[39m
[38;5;12m  - [39m[38;5;12mpaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1710.09599.pdf)[39m
[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (http://sami.haija.org/graph/context)[39m
[38;5;12m  - [39m[38;5;12mPython PyTorch[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/AttentionWalk)[39m
[38;5;12m  - [39m[38;5;12mPython TensorFlow[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/google-research/google-research/tree/master/graph_embedding/watch_your_step/)[39m
[38;5;12m- [39m[38;5;14m[1mGAT[0m
[38;5;12m  - Graph Attention Networks, ICLR'18[39m
[38;5;12m  - [39m[38;5;12mpaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1710.10903.pdf)[39m
[38;5;12m  - [39m[38;5;12mPython PyTorch[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/Diego999/pyGAT)[39m
[38;5;12m  - [39m[38;5;12mPython TensorFlow[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/PetarV-/GAT)[39m
[38;5;12m- [39m[38;5;14m[1mSINE[0m
[38;5;12m  - SINE: Scalable Incomplete Network Embedding, ICDM'18[39m
[38;5;12m  - [39m[38;5;12mpaper[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/SINE/blob/master/paper.pdf)[39m
[38;5;12m  - [39m[38;5;12mPython KarateClub[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/karateclub)[39m
[38;5;12m  - [39m[38;5;12mPython PyTorch[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/SINE/)[39m
[38;5;12m  - [39m[38;5;12mC++[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/daokunzhang/SINE)[39m
[38;5;12m- [39m[38;5;14m[1mSGCN[0m
[38;5;12m  - Signed Graph Convolutional Network, ICDM'18[39m
[38;5;12m  - [39m[38;5;12mpaper[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/SGCN/blob/master/sgcn.pdf)[39m
[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/SGCN)[39m
[38;5;12m- [39m[38;5;14m[1mTENE[0m
[38;5;12m  - Enhanced Network Embedding with Text Information, ICPR'18[39m
[38;5;12m  - [39m[38;5;12mpaper[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/TENE/blob/master/tene_paper.pdf)[39m
[38;5;12m  - [39m[38;5;12mPython KarateClub[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/karateclub)[39m
[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/TENE) [39m
[38;5;12m- [39m[38;5;14m[1mDANMF[0m
[38;5;12m  - Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection, CIKM'18[39m
[38;5;12m  - [39m[38;5;12mpaper[39m[38;5;14m[1m [0m[38;5;12m (https://smartyfh.com/Documents/18DANMF.pdf)[39m
[38;5;12m  - [39m[38;5;12mPython KarateClub[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/karateclub)[39m
[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/DANMF)[39m
[38;5;12m  - [39m[38;5;12mMatlab[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/smartyfh/DANMF)  [39m
[38;5;12m- [39m[38;5;14m[1mBANE[0m
[38;5;12m  - Binarized Attributed Network Embedding, ICDM'18[39m
[38;5;12m  - [39m[38;5;12mpaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.researchgate.net/publication/328688614_Binarized_Attributed_Network_Embedding)[39m
[38;5;12m  - [39m[38;5;12mPython KarateClub[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/karateclub)[39m
[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/BANE)[39m
[38;5;12m  - [39m[38;5;12mMatlab[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/ICDM2018-BANE/BANE)[39m
[38;5;12m- [39m[38;5;14m[1mGCN Insights[0m
[38;5;12m  - Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning, AAAI'18[39m
[38;5;12m  - [39m[38;5;12mProject[39m[38;5;14m[1m [0m[38;5;12m (https://liqimai.github.io/blog/AAAI-18/)[39m
[38;5;12m  - [39m[38;5;12mcode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/liqimai/gcn/tree/AAAI-18/)[39m
[38;5;12m- [39m[38;5;14m[1mPCTADW[0m
[38;5;12m  - Learning Embeddings of Directed Networks with Text-Associated Nodes---with Applications in Software Package Dependency Networks[39m
[38;5;12m  - [39m[38;5;12mpaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1809.02270.pdf)[39m
[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/shudan/PCTADW)[39m
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[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/walklets)  [39m
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[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/snash4/GAT2VEC)[39m
[38;5;12m- [39m[38;5;14m[1mFSCNMF[0m
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[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/sambaranban/FSCNMF)  [39m
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[38;5;12m- [39m[38;5;14m[1mSIDE[0m
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[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/nd7141/Anonymous-Walk-Embeddings)[39m
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[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/AnryYang/HOPE)[39m
[38;5;12m  - [39m[38;5;12mPython KarateClub[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/karateclub) [39m
[38;5;12m- [39m[38;5;14m[1mVERSE[0m
[38;5;12m  - VERSE, Versatile Graph Embeddings from Similarity Measures[39m
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[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/xgfs/verse) [39m
[38;5;12m- [39m[38;5;14m[1mAGNN[0m
[38;5;12m  - Attention-based Graph Neural Network for semi-supervised learning[39m
[38;5;12m  - [39m[38;5;12mICLR 2018 OpenReview (rejected)[39m[38;5;14m[1m [0m[38;5;12m (https://openreview.net/forum?id=rJg4YGWRb)[39m
[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/dawnranger/pytorch-AGNN)[39m
[38;5;12m- [39m[38;5;14m[1mSEANO[0m
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[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1703.08100.pdf) (SDM 2018)[39m
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[38;5;12m- [39m[38;5;14m[1mHyperbolics[0m
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[38;5;12m- [39m[38;5;14m[1mDGCNN[0m
[38;5;12m  - An End-to-End Deep Learning Architecture for Graph Classiﬁcation [39m
[38;5;12m  - [39m[38;5;12mAAAI 2018[39m[38;5;14m[1m [0m[38;5;12m (http://www.cse.wustl.edu/~muhan/papers/AAAI_2018_DGCNN.pdf)[39m
[38;5;12m  - [39m[38;5;12mLua[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/muhanzhang/DGCNN) [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/muhanzhang/pytorch_DGCNN)  [39m
[38;5;12m- [39m[38;5;14m[1mstructure2vec[0m
[38;5;12m  - Discriminative Embeddings of Latent Variable Models for Structured Data [39m
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[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/Hanjun-Dai/pytorch_structure2vec)  [39m
[38;5;12m- [39m[38;5;14m[1mDecagon[0m
[38;5;12m  - Decagon, Graph Neural Network for Multirelational Link Prediction [39m
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[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/marinkaz/decagon)    [39m
[38;5;12m- [39m[38;5;14m[1mDHNE[0m
[38;5;12m  - Structural Deep Embedding for Hyper-Networks[39m
[38;5;12m  - [39m[38;5;12mAAAI 2018[39m[38;5;14m[1m [0m[38;5;12m (http://nrl.thumedialab.com/Structural-Deep-Embedding-for-Hyper-Networks)[39m[38;5;12mArxiv[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1711.10146)[39m
[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/tadpole/DHNE)  [39m
[38;5;12m- [39m[38;5;14m[1mOhmnet[0m
[38;5;12m  - Feature Learning in Multi-Layer Networks [39m
[38;5;12m  - [39m[38;5;12mArxiv[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1707.04638) [39m[38;5;12mSNAP[39m[38;5;14m[1m [0m[38;5;12m (http://snap.stanford.edu/ohmnet/) [39m
[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/marinkaz/ohmnet)  [39m
[38;5;12m- [39m[38;5;14m[1mSDNE[0m
[38;5;12m  - Structural Deep Network Embedding [39m
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[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/xiaohan2012/sdne-keras) [39m
[38;5;12m- [39m[38;5;14m[1mSTWalk[0m
[38;5;12m  - STWalk: Learning Trajectory Representations in Temporal Graphs[39m[38;5;14m[1m  [0m
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[38;5;12m- [39m[38;5;14m[1mLoNGAE[0m
[38;5;12m  - Learning to Make Predictions on Graphs with Autoencoders [39m
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[38;5;12m  - [39m[38;5;12mMatlab[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/zhengwang100/RSDNE) [39m
[38;5;12m- [39m[38;5;14m[1mFastGCN[0m
[38;5;12m  - FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling [39m
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[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/matenure/FastGCN)[39m
[38;5;12m- [39m[38;5;14m[1mdiff2vec[0m
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[38;5;12m  - [39m[38;5;12mPython KarateClub[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/karateclub)[39m
[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/diff2vec) [39m
[38;5;12m- [39m[38;5;14m[1mPoincare[0m
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[38;5;12m  - [39m[38;5;12mPyTorch[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/facebookresearch/poincare-embeddings) [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://radimrehurek.com/gensim/models/poincare.html) [39m[38;5;12mC++[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/TatsuyaShirakawa/poincare-embedding)[39m
[38;5;12m- [39m[38;5;14m[1mPEUNE[0m
[38;5;12m  - [39m[38;5;14m[1mPRUNE: Preserving Proximity and Global Ranking for Network Embedding[0m[38;5;12m (https://papers.nips.cc/paper/7110-prune-preserving-proximity-and-global-ranking-for-network-embedding), NIPS 2017[39m
[38;5;12m  - [39m[38;5;12mcode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/ntumslab/PRUNE)[39m
[38;5;12m- [39m[38;5;14m[1mASNE[0m
[38;5;12m  - Attributed Social Network Embedding, TKDE'18[39m
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[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/lizi-git/ASNE)[39m
[38;5;12m  - [39m[38;5;12mFast Python[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/ASNE)[39m
[38;5;12m- [39m[38;5;14m[1mGraphWave[0m
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[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/snap-stanford/graphwave) [39m[38;5;12mfaster version[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/GraphWaveMachine)[39m
[38;5;12m- [39m[38;5;14m[1mStarSpace[0m
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[38;5;12m- [39m[38;5;14m[1mproNet-core[0m
[38;5;12m  - Vertex-Context Sampling for Weighted Network Embedding, arxiv'17[39m
[38;5;12m  - [39m[38;5;12marxiv[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1711.00227) [39m[38;5;12mcode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/cnclabs/proNet-core)[39m
[38;5;12m- [39m[38;5;14m[1mstruc2vec[0m
[38;5;12m  - [39m[38;5;14m[1mstruc2vec: Learning Node Representations from Structural Identity[0m[38;5;12m (https://dl.acm.org/citation.cfm?id=3098061), KDD'17[39m
[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/leoribeiro/struc2vec)[39m
[38;5;12m- [39m[38;5;14m[1mComE[0m
[38;5;12m  - Learning Community Embedding with Community Detection and Node Embedding on Graphs, CIKM'17[39m
[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/andompesta/ComE)[39m
[38;5;12m- [39m[38;5;14m[1mBoostedNE[0m
[38;5;12m  - [39m[38;5;14m[1mMulti-Level Network Embedding with Boosted Low-Rank Matrix Approximation[0m[38;5;12m (https://arxiv.org/abs/1808.08627), '18[39m
[38;5;12m  - [39m[38;5;12mPython KarateClub[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/karateclub)[39m
[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/BoostedFactorization)[39m
[38;5;12m- [39m[38;5;14m[1mM-NMF[0m
[38;5;12m  - Community Preserving Network Embedding, AAAI'17[39m
[38;5;12m  - [39m[38;5;12mPython TensorFlow[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/M-NMF)[39m
[38;5;12m  - [39m[38;5;12mPython KarateClub[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/karateclub)[39m
[38;5;12m- [39m[38;5;14m[1mGraphSAGE[0m
[38;5;12m  - Inductive Representation Learning on Large Graphs, NIPS'17[39m
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[38;5;12m- [39m[38;5;14m[1mICE[0m
[38;5;12m  - [39m[38;5;14m[1mICE: Item Concept Embedding via Textual Information[0m[38;5;12m (http://dl.acm.org/citation.cfm?id=3080807), SIGIR'17[39m
[38;5;12m  - [39m[38;5;12mdemo[39m[38;5;14m[1m [0m[38;5;12m (https://cnclabs.github.io/ICE/) [39m[38;5;12mcode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/cnclabs/ICE)[39m
[38;5;12m- [39m[38;5;14m[1mGuidedHeteEmbedding[0m
[38;5;12m  - Task-guided and path-augmented heterogeneous network embedding for author identification, WSDM'17[39m
[38;5;12m  - [39m[38;5;12mpaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1612.02814.pdf) [39m[38;5;12mcode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/chentingpc/GuidedHeteEmbedding)[39m
[38;5;12m- [39m[38;5;14m[1mmetapath2vec[0m
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[38;5;12m- [39m[38;5;14m[1mGCN[0m
[38;5;12m  - Semi-Supervised Classification with Graph Convolutional Networks, ICLR'17[39m
[38;5;12m  - [39m[38;5;12marxiv[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1609.02907)  [39m[38;5;12mPython Tensorflow[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/tkipf/gcn)[39m
[38;5;12m- [39m[38;5;14m[1mGAE[0m
[38;5;12m  - Variational Graph Auto-Encoders, arxiv[39m
[38;5;12m  - [39m[38;5;12marxiv[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1611.07308) [39m[38;5;12mPython Tensorflow[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/tkipf/gae)[39m
[38;5;12m- [39m[38;5;14m[1mCANE[0m
[38;5;12m  - CANE: Context-Aware Network Embedding for Relation Modeling, ACL'17[39m
[38;5;12m  - [39m[38;5;12mpaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.thunlp.org/~tcc/publications/acl2017_cane.pdf) [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/thunlp/cane)[39m
[38;5;12m- [39m[38;5;14m[1mTransNet[0m
[38;5;12m  - TransNet: Translation-Based Network Representation Learning for Social Relation Extraction, IJCAI'17[39m
[38;5;12m  - [39m[38;5;12mPython Tensorflow[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/thunlp/TransNet)[39m
[38;5;12m- [39m[38;5;14m[1mcnn_graph[0m
[38;5;12m  - Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering, NIPS'16[39m
[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/mdeff/cnn_graph)[39m
[38;5;12m- [39m[38;5;14m[1mConvE[0m
[38;5;12m  - [39m[38;5;14m[1mConvolutional 2D Knowledge Graph Embeddings[0m[38;5;12m (https://arxiv.org/pdf/1707.01476v2.pdf), arxiv[39m
[38;5;12m  - [39m[38;5;12msource[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/TimDettmers/ConvE)[39m
[38;5;12m- [39m[38;5;14m[1mnode2vec[0m
[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
[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
[38;5;12m- [39m[38;5;14m[1mDNGR[0m
[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
[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
[38;5;12m- [39m[38;5;14m[1mHolE[0m
[38;5;12m  - [39m[38;5;14m[1mHolographic Embeddings of Knowledge Graphs[0m[38;5;12m (http://dl.acm.org/citation.cfm?id=3016172), AAAI'16[39m
[38;5;12m  - [39m[38;5;12mPython-sklearn[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/mnick/holographic-embeddings) [39m[38;5;12mPython-sklearn2[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/mnick/scikit-kge)[39m
[38;5;12m- [39m[38;5;14m[1mComplEx[0m
[38;5;12m  - [39m[38;5;14m[1mComplex Embeddings for Simple Link Prediction[0m[38;5;12m (http://dl.acm.org/citation.cfm?id=3045609), ICML'16[39m
[38;5;12m  - [39m[38;5;12marxiv[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1606.06357) [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/ttrouill/complex)[39m
[38;5;12m- [39m[38;5;14m[1mMMDW[0m
[38;5;12m  - Max-Margin DeepWalk: Discriminative Learning of Network Representation, IJCAI'16[39m
[38;5;12m  - [39m[38;5;12mpaper[39m[38;5;14m[1m [0m[38;5;12m (http://nlp.csai.tsinghua.edu.cn/~lzy/publications/ijcai2016_mmdw.pdf)  [39m[38;5;12mJava[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/thunlp/MMDW)[39m
[38;5;12m- [39m[38;5;14m[1mplanetoid[0m
[38;5;12m  - Revisiting Semi-supervised Learning with Graph Embeddings, ICML'16[39m
[38;5;12m  - [39m[38;5;12marxiv[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1603.08861) [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/kimiyoung/planetoid)[39m
[38;5;12m- [39m[38;5;14m[1mgraph2vec[0m
[38;5;12m  - graph2vec: Learning Distributed Representations of Graphs, KDD'17 MLGWorkshop[39m
[38;5;12m  - [39m[38;5;12marxiv[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1707.05005)[39m
[38;5;12m  - [39m[38;5;12mPython gensim[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/graph2vec) [39m[38;5;12mPython KarateClub[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/karateclub)[39m
[38;5;12m- [39m[38;5;14m[1mPowerWalk[0m
[38;5;12m  - [39m[38;5;14m[1mPowerWalk: Scalable Personalized PageRank via Random Walks with Vertex-Centric Decomposition[0m[38;5;12m (http://dl.acm.org/citation.cfm?id=2983713), CIKM'16[39m
[38;5;12m  - [39m[38;5;12mcode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/lqhl/PowerWalk)[39m
[38;5;12m- [39m[38;5;14m[1mLINE[0m
[38;5;12m  - [39m[38;5;14m[1mLINE: Large-scale information network embedding[0m[38;5;12m (http://dl.acm.org/citation.cfm?id=2741093), WWW'15[39m
[38;5;12m  - [39m[38;5;12marxiv[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1503.03578) [39m[38;5;12mC++[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/tangjianpku/LINE) [39m[38;5;12mPython TF[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/snowkylin/line) [39m[38;5;12mPython Theano/Keras[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/VahidooX/LINE)[39m
[38;5;12m- [39m[38;5;14m[1mPTE[0m
[38;5;12m  - [39m[38;5;14m[1mPTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks[0m[38;5;12m (http://dl.acm.org/citation.cfm?id=2783307), KDD'15[39m
[38;5;12m  - [39m[38;5;12mC++[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/mnqu/PTE)[39m
[38;5;12m- [39m[38;5;14m[1mGraRep[0m
[38;5;12m  - [39m[38;5;14m[1mGrarep: Learning graph representations with global structural information[0m[38;5;12m (http://dl.acm.org/citation.cfm?id=2806512), CIKM'15[39m
[38;5;12m  - [39m[38;5;12mMatlab[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/ShelsonCao/GraRep)[39m
[38;5;12m  - [39m[38;5;12mJulia[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/xgfs/GraRep.jl)[39m
[38;5;12m  - [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/GraRep)[39m
[38;5;12m  - [39m[38;5;12mPython KarateClub[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/karateclub)[39m
[38;5;12m- [39m[38;5;14m[1mKB2E[0m
[38;5;12m  - [39m[38;5;14m[1mLearning Entity and Relation Embeddings for Knowledge Graph Completion[0m[38;5;12m (http://dl.acm.org/citation.cfm?id=2886624), AAAI'15[39m
[38;5;12m  - [39m[38;5;12mpaper[39m[38;5;14m[1m [0m[38;5;12m (http://nlp.csai.tsinghua.edu.cn/~lzy/publications/aaai2015_transr.pdf) [39m[38;5;12mC++[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/thunlp/KB2E)  [39m[38;5;12mfaster version[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/thunlp/Fast-TransX)[39m
[38;5;12m- [39m[38;5;14m[1mTADW[0m
[38;5;12m  - [39m[38;5;14m[1mNetwork Representation Learning with Rich Text Information[0m[38;5;12m (http://dl.acm.org/citation.cfm?id=2832542), IJCAI'15[39m
[38;5;12m  - [39m[38;5;12mpaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/Proceedings/15/Papers/299.pdf) [39m[38;5;12mMatlab[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/thunlp/tadw) [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/benedekrozemberczki/TADW)[39m
[38;5;12m- [39m[38;5;14m[1mDeepWalk[0m
[38;5;12m  - [39m[38;5;14m[1mDeepWalk: Online Learning of Social Representations[0m[38;5;12m (http://dl.acm.org/citation.cfm?id=2623732), KDD'14[39m
[38;5;12m  - [39m[38;5;12marxiv[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1403.6652) [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/phanein/deepwalk)  [39m[38;5;12mC++[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/xgfs/deepwalk-c)[39m
[38;5;12m- [39m[38;5;14m[1mGEM[0m
[38;5;12m  - Graph Embedding Techniques, Applications, and Performance: A Survey[39m
[38;5;12m  - [39m[38;5;12marxiv[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1705.02801) [39m[38;5;12mPython[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/palash1992/GEM)[39m
[38;5;12m- [39m[38;5;14m[1mDNE-SBP[0m
[38;5;12m  - Deep Network Embedding for Graph Representation Learning in Signed Networks[39m
[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

[38;5;12m                                                                                                              [39m[38;2;255;187;0m[1m[4mPaper References[0m

[38;5;14m[1mA Comprehensive Survey on Graph Neural Networks[0m[38;5;12m (https://arxiv.org/abs/1901.00596), arxiv'19[39m

[38;5;14m[1mHierarchical Graph Representation Learning with Differentiable Pooling[0m[38;5;12m (https://arxiv.org/pdf/1806.08804.pdf), NIPS'18[39m

[38;5;14m[1mSEMAC[0m[38;5;12m,[39m[38;5;12m [39m[38;5;14m[1mLink[0m[38;5;14m[1m [0m[38;5;14m[1mPrediction[0m[38;5;14m[1m [0m[38;5;14m[1mvia[0m[38;5;14m[1m [0m[38;5;14m[1mSubgraph[0m[38;5;14m[1m [0m[38;5;14m[1mEmbedding-Based[0m[38;5;14m[1m [0m[38;5;14m[1mConvex[0m[38;5;14m[1m [0m[38;5;14m[1mMatrix[0m[38;5;14m[1m [0m[38;5;14m[1mCompletion[0m[38;5;12m [39m[38;5;12m(https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16442),[39m[38;5;12m [39m[38;5;12mAAAI[39m[38;5;12m [39m[38;5;12m2018,[39m[38;5;12m [39m[38;5;14m[1mSlides[0m[38;5;12m [39m
[38;5;12m(https://www.slideshare.net/gdm3003/semac-graph-node-embeddings-for-link-prediction)[39m

[38;5;14m[1mMILE[0m[38;5;12m, [39m[38;5;14m[1mMILE: A Multi-Level Framework for Scalable Graph Embedding[0m[38;5;12m (https://arxiv.org/pdf/1802.09612.pdf), arxiv'18[39m

[38;5;14m[1mMetaGraph2Vec[0m[38;5;12m, [39m[38;5;14m[1mMetaGraph2Vec: Complex Semantic Path Augmented Heterogeneous Network Embedding[0m[38;5;12m (https://arxiv.org/abs/1803.02533)[39m

[38;5;14m[1mPinSAGE[0m[38;5;12m, [39m[38;5;14m[1mGraph Convolutional Neural Networks for Web-Scale Recommender Systems[0m[38;5;12m (https://arxiv.org/abs/1806.01973)[39m

[38;5;14m[1mCurriculum Learning for Heterogeneous Star Network Embedding via Deep Reinforcement Learning[0m[38;5;12m (https://dl.acm.org/citation.cfm?id=3159711), WSDM '18[39m

[38;5;14m[1mAdversarial Network Embedding[0m[38;5;12m (https://arxiv.org/abs/1711.07838), arxiv[39m

[38;5;14m[1mRole2Vec[0m[38;5;12m, [39m[38;5;14m[1mLearning Role-based Graph Embeddings[0m[38;5;12m (https://arxiv.org/abs/1802.02896)[39m

[38;5;14m[1medge2vec[0m[38;5;12m, [39m[38;5;14m[1mFeature Propagation on Graph: A New Perspective to Graph Representation[0m
[38;5;12mLearning[39m[38;5;14m[1m (https://arxiv.org/abs/1804.06111)[0m

[38;5;14m[1mMINES[0m[38;5;12m, [39m[38;5;14m[1mMulti-Dimensional Network Embedding with Hierarchical Structure[0m[38;5;12m (http://cse.msu.edu/~mayao4/downloads/Multidimensional_Network_Embedding_with_Hierarchical_Structure.pdf)[39m

[38;5;14m[1mWalk-Steered Convolution for Graph Classification[0m[38;5;12m (https://arxiv.org/abs/1804.05837)[39m

[38;5;14m[1mDeep Feature Learning for Graphs[0m[38;5;12m (https://arxiv.org/abs/1704.08829), arxiv'17[39m

[38;5;14m[1mFast Linear Model for Knowledge Graph Embeddings[0m[38;5;12m (https://arxiv.org/abs/1710.10881), arxiv'17[39m

[38;5;14m[1mNetwork Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec[0m[38;5;12m (https://arxiv.org/abs/1710.02971), arxiv'17[39m

[38;5;14m[1mA Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications[0m[38;5;12m (https://arxiv.org/abs/1709.07604), arxiv'17[39m

[38;5;14m[1mRepresentation Learning on Graphs: Methods and Applications[0m[38;5;12m (https://arxiv.org/pdf/1709.05584.pdf), IEEE DEB'17[39m

[38;5;14m[1mCONE[0m[38;5;12m, [39m[38;5;14m[1mCONE: Community Oriented Network Embedding[0m[38;5;12m (https://arxiv.org/abs/1709.01554), arxiv'17[39m

[38;5;14m[1mLANE[0m[38;5;12m, [39m
[38;5;14m[1mLabel Informed Attributed Network Embedding[0m[38;5;12m (http://dl.acm.org/citation.cfm?id=3018667), WSDM'17[39m

[38;5;14m[1mGraph2Gauss[0m[38;5;12m,[39m
[38;5;14m[1mDeep Gaussian Embedding of Attributed Graphs: Unsupervised Inductive Learning via Ranking[0m[38;5;12m (https://arxiv.org/abs/1707.03815), arxiv[39m
[38;5;12mBonus Animation[39m[38;5;14m[1m [0m[38;5;12m (https://twitter.com/abojchevski/status/885502050133585925)[39m

[38;5;14m[1mScalable Graph Embedding for Asymmetric Proximity[0m[38;5;12m (https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14696), AAAI'17[39m

[38;5;14m[1mQuery-based Music Recommendations via Preference Embedding[0m[38;5;12m (http://dl.acm.org/citation.cfm?id=2959169), RecSys'16[39m

[38;5;14m[1mTri-party deep network representation[0m[38;5;12m (http://dl.acm.org/citation.cfm?id=3060886), IJCAI'16[39m

[38;5;14m[1mHeterogeneous Network Embedding via Deep Architectures[0m[38;5;12m (http://dl.acm.org/citation.cfm?id=2783296), KDD'15[39m

[38;5;14m[1mNeural Word Embedding As Implicit Matrix Factorization[0m[38;5;12m (http://dl.acm.org/citation.cfm?id=2969070), NIPS'14[39m

[38;5;14m[1mDistributed large-scale natural graph factorization[0m[38;5;12m (http://dl.acm.org/citation.cfm?id=2488393), WWW'13[39m

[38;5;14m[1mFrom Node Embedding To Community Embedding[0m[38;5;12m (https://arxiv.org/abs/1610.09950), arxiv[39m

[38;5;14m[1mWalklets: Multiscale Graph Embeddings for Interpretable Network Classification[0m[38;5;12m (https://arxiv.org/abs/1605.02115), arxiv[39m

[38;5;14m[1mComprehend DeepWalk as Matrix Factorization[0m[38;5;12m (https://arxiv.org/abs/1501.00358), arxiv[39m

[38;5;12m                                                                                                           [39m[38;2;255;187;0m[1m[4mConference & Workshop[0m

[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

[38;5;14m[1mSMORe : Modularize Graph Embedding for Recommendation[0m[38;5;12m (https://github.com/cnclabs/smore), [39m[38;5;14m[1mRecSys'19[0m

[38;5;14m[1m13th International Workshop on Mining and Learning with Graphs[0m[38;5;12m (http://www.mlgworkshop.org/2017/), [39m[38;5;14m[1mMLG'17[0m

[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

[38;5;12m                                                                                                                [39m[38;2;255;187;0m[1m[4mRelated List[0m

[38;5;14m[1mawesome-graph-classification[0m[38;5;12m (https://github.com/benedekrozemberczki/awesome-graph-classification)[39m

[38;5;14m[1mawesome-community-detection[0m[38;5;12m (https://github.com/benedekrozemberczki/awesome-community-detection)[39m

[38;5;14m[1mawesome-embedding-models[0m[38;5;12m (https://github.com/Hironsan/awesome-embedding-models)[39m

[38;5;14m[1mMust-read papers on network representation learning (NRL) / network embedding (NE)[0m[38;5;12m (https://github.com/thunlp/NRLPapers)[39m

[38;5;14m[1mMust-read papers on knowledge representation learning (KRL) / knowledge embedding (KE)[0m[38;5;12m (https://github.com/thunlp/KRLPapers)[39m

[38;5;14m[1mNetwork Embedding Resources[0m[38;5;12m (https://github.com/nate-russell/Network-Embedding-Resources)[39m

[38;5;14m[1mawesome-embedding-models[0m[38;5;12m (https://github.com/Hironsan/awesome-embedding-models)[39m

[38;5;14m[1m2vec-type embedding models[0m[38;5;12m (https://github.com/MaxwellRebo/awesome-2vec)[39m

[38;5;14m[1mMust-read papers on GNN[0m[38;5;12m (https://github.com/thunlp/GNNPapers)[39m

[38;5;14m[1mLiteratureDL4Graph[0m[38;5;12m (https://github.com/DeepGraphLearning/LiteratureDL4Graph)[39m

[38;5;14m[1mawesome-graph-classification[0m[38;5;12m (https://github.com/benedekrozemberczki/awesome-graph-classification)[39m

[38;5;12m                                                                                                              [39m[38;2;255;187;0m[1m[4mRelated Project[0m

[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

[38;5;14m[1mStellarGraph Machine Learning Library[0m[38;5;12m [39m[38;5;14m[1mwebsite[0m[38;5;12m (https://www.stellargraph.io) [39m[38;5;14m[1mGitHub[0m[38;5;12m (https://github.com/stellargraph/stellargraph)[39m
