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awesome-network-embedding\n[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)\n[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)\n[![Gitter chat for developers at https://gitter.im/dmlc/xgboost](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/awesome-network-embedding/Lobby)\n\nAlso called network representation learning, graph embedding, knowledge embedding, etc.\n\nThe task is to learn the representations of the vertices from a given network.\n\nCALL 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:)\n\n\u003cimg src=\"NE.png\" width=\"480\"\u003e\n\n# Paper References with the implementation(s)\n- **GraphGym**\n  - A platform for designing and evaluating Graph Neural Networks (GNN), NeurIPS 2020\n  - [[Paper]](https://proceedings.neurips.cc/paper/2020/file/c5c3d4fe6b2cc463c7d7ecba17cc9de7-Paper.pdf)\n  - [[Python]](https://github.com/snap-stanford/graphgym)\n- **FEATHER**\n  - Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models, CIKM 2020\n  - [[Paper]](https://arxiv.org/abs/2005.07959)\n  - [[Python]](https://github.com/benedekrozemberczki/FEATHER)\n  - [[Python KarateClub]](https://github.com/benedekrozemberczki/karateclub)\n- **HeGAN**\n  - Adversarial Learning on Heterogeneous Information Networks, KDD 2019\n  - [[Paper]](https://fangyuan1st.github.io/paper/KDD19_HeGAN.pdf)\n  - [[Python]](https://github.com/librahu/HeGAN)\n- **NetMF**\n  - Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and Node2Vec, WSDM 2018\n  - [[Paper]](https://keg.cs.tsinghua.edu.cn/jietang/publications/WSDM18-Qiu-et-al-NetMF-network-embedding.pdf)\n  - [[Python KarateClub]](https://github.com/benedekrozemberczki/karateclub)\n- **GL2Vec**\n  - GL2vec: Graph Embedding Enriched by Line Graphs with Edge Features, ICONIP 2019\n  - [[Paper]](https://link.springer.com/chapter/10.1007/978-3-030-36718-3_1)\n  - [[Python KarateClub]](https://github.com/benedekrozemberczki/karateclub)\n- **NNSED**\n  - A Non-negative Symmetric Encoder-Decoder Approach for Community Detection, CIKM 2017\n  - [[Paper]](http://www.bigdatalab.ac.cn/~shenhuawei/publications/2017/cikm-sun.pdf)\n  - [[Python KarateClub]](https://github.com/benedekrozemberczki/karateclub) \n- **SymmNMF**\n  - Symmetric Nonnegative Matrix Factorization for Graph Clustering, SDM 2012\n  - [[Paper]](https://www.cc.gatech.edu/~hpark/papers/DaDingParkSDM12.pdf)\n  - [[Python KarateClub]](https://github.com/benedekrozemberczki/karateclub)\n- **RECT**\n  - Network Embedding with Completely-Imbalanced Labels, TKDE 2020\n  - [[Paper]](https://zhengwang100.github.io/pdf/TKDE20_wzheng.pdf)\n  - [[Python]](https://github.com/zhengwang100/RECT) \n- **GEMSEC**\n  - GEMSEC: Graph Embedding with Self Clustering, ASONAM 2019\n  - [[Paper]](https://arxiv.org/abs/1802.03997)\n  - [[Python]](https://github.com/benedekrozemberczki/GEMSEC) \n- **AmpliGraph**\n  - Library for learning knowledge graph embeddings with TensorFlow \n  - [[Project]](http://docs.ampligraph.org)\n  - [[code]](https://github.com/Accenture/AmpliGraph)\n- **jodie**\n  - Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks, KDD'19\n  - [[Project]](http://snap.stanford.edu/jodie/)\n  - [[Code]](https://github.com/srijankr/jodie/)\n- **PyTorch-BigGraph**\n  - Pytorch-BigGraph - a distributed system for learning graph embeddings for large graphs, SysML'19\n  - [[github]](https://github.com/facebookresearch/PyTorch-BigGraph)\n- **ATP**\n  - ATP: Directed Graph Embedding with Asymmetric Transitivity Preservation, AAAI'19\n  - [[paper]](https://arxiv.org/abs/1811.00839)\n  - [[code]](https://github.com/zhenv5/atp)\n- **MUSAE**\n  - Multi-scale Attributed Node Embedding, ArXiv 2019\n  - [[paper]](https://arxiv.org/abs/1909.13021)\n  - [[Python KarateClub]](https://github.com/benedekrozemberczki/karateclub)\n  - [[Python]](https://github.com/benedekrozemberczki/MUSAE)\n- **SEAL-CI**\n  - Semi-Supervised Graph Classification: A Hierarchical Graph Perspective, WWW'19\n  - [[paper]](https://arxiv.org/pdf/1904.05003.pdf)\n  - [[Python PyTorch]](https://github.com/benedekrozemberczki/SEAL-CI)\n- **N-GCN and MixHop**\n  - A Higher-Order Graph Convolutional Layer, NIPS'18 (workshop)\n  - [[paper]](http://sami.haija.org/papers/high-order-gc-layer.pdf)\n  - [[Python PyTorch]](https://github.com/benedekrozemberczki/MixHop-and-N-GCN)\n- **CapsGNN**\n  - Capsule Graph Neural Network, ICLR'19\n  - [[paper]](https://openreview.net/forum?id=Byl8BnRcYm)\n  - [[Python PyTorch]](https://github.com/benedekrozemberczki/CapsGNN)\n- **Splitter**\n  - Splitter: Learning Node Representations that Capture Multiple Social Contexts, WWW'19\n  - [[paper]](http://epasto.org/papers/www2019splitter.pdf)\n  - [[Python PyTorch]](https://github.com/benedekrozemberczki/Splitter)\n- **REGAL**\n  - REGAL: Representation Learning-based Graph Alignment. International Conference on Information and Knowledge Management, CIKM'18\n  - [[arxiv]](https://arxiv.org/pdf/1802.06257.pdf)\n  - [[paper]](https://dl.acm.org/citation.cfm?id=3271788)\n  - [[code]](https://github.com/GemsLab/REGAL)\n- **PyTorch Geometric**\n  - Fast Graph Representation Learning With PyTorch Geometric\n  - [[paper]](https://arxiv.org/pdf/1903.02428.pdf)\n  - [[Python PyTorch]](https://github.com/rusty1s/pytorch_geometric)\n- **TuckER**\n  - Tensor Factorization for Knowledge Graph Completion, Arxiv'19\n  - [[paper]](https://arxiv.org/pdf/1901.09590.pdf)\n  - [[Python PyTorch]](https://github.com/ibalazevic/TuckER)\n- **HypER**\n  - Hypernetwork Knowledge Graph Embeddings, Arxiv'18\n  - [[paper]](https://arxiv.org/pdf/1808.07018.pdf)\n  - [[Python PyTorch]](https://github.com/ibalazevic/HypER)\n- **GWNN**\n  - Graph Wavelet Neural Network, ICLR'19\n  - [[paper]](https://openreview.net/forum?id=H1ewdiR5tQ)\n  - [[Python PyTorch]](https://github.com/benedekrozemberczki/GraphWaveletNeuralNetwork)\n  - [[Python TensorFlow]](https://github.com/Eilene/GWNN)\n- **APPNP**\n  - Combining Neural Networks with Personalized PageRank for Classification on Graphs, ICLR'19\n  - [[paper]](https://arxiv.org/abs/1810.05997)\n  - [[Python PyTorch]](https://github.com/benedekrozemberczki/APPNP)\n  - [[Python TensorFlow]](https://github.com/klicperajo/ppnp)\n- **role2vec**\n  - Learning Role-based Graph Embeddings, IJCAI'18\n  - [[paper]](https://arxiv.org/pdf/1802.02896.pdf)\n  - [[Python KarateClub]](https://github.com/benedekrozemberczki/karateclub)\n  - [[Python]](https://github.com/benedekrozemberczki/role2vec)\n- **AttentionWalk**\n  - Watch Your Step: Learning Node Embeddings via Graph Attention, NIPS'18\n  - [[paper]](https://arxiv.org/pdf/1710.09599.pdf)\n  - [[Python]](http://sami.haija.org/graph/context)\n  - [[Python PyTorch]](https://github.com/benedekrozemberczki/AttentionWalk)\n  - [[Python TensorFlow]](https://github.com/google-research/google-research/tree/master/graph_embedding/watch_your_step/)\n- **GAT**\n  - Graph Attention Networks, ICLR'18\n  - [[paper]](https://arxiv.org/pdf/1710.10903.pdf)\n  - [[Python PyTorch]](https://github.com/Diego999/pyGAT)\n  - [[Python TensorFlow]](https://github.com/PetarV-/GAT)\n- **SINE**\n  - SINE: Scalable Incomplete Network Embedding, ICDM'18\n  - [[paper]](https://github.com/benedekrozemberczki/SINE/blob/master/paper.pdf)\n  - [[Python KarateClub]](https://github.com/benedekrozemberczki/karateclub)\n  - [[Python PyTorch]](https://github.com/benedekrozemberczki/SINE/)\n  - [[C++]](https://github.com/daokunzhang/SINE)\n- **SGCN**\n  - Signed Graph Convolutional Network, ICDM'18\n  - [[paper]](https://github.com/benedekrozemberczki/SGCN/blob/master/sgcn.pdf)\n  - [[Python]](https://github.com/benedekrozemberczki/SGCN)\n- **TENE**\n  - Enhanced Network Embedding with Text Information, ICPR'18\n  - [[paper]](https://github.com/benedekrozemberczki/TENE/blob/master/tene_paper.pdf)\n  - [[Python KarateClub]](https://github.com/benedekrozemberczki/karateclub)\n  - [[Python]](https://github.com/benedekrozemberczki/TENE) \n- **DANMF**\n  - Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection, CIKM'18\n  - [[paper]](https://smartyfh.com/Documents/18DANMF.pdf)\n  - [[Python KarateClub]](https://github.com/benedekrozemberczki/karateclub)\n  - [[Python]](https://github.com/benedekrozemberczki/DANMF)\n  - [[Matlab]](https://github.com/smartyfh/DANMF)  \n- **BANE**\n  - Binarized Attributed Network Embedding, ICDM'18\n  - [[paper]](https://www.researchgate.net/publication/328688614_Binarized_Attributed_Network_Embedding)\n  - [[Python KarateClub]](https://github.com/benedekrozemberczki/karateclub)\n  - [[Python]](https://github.com/benedekrozemberczki/BANE)\n  - [[Matlab]](https://github.com/ICDM2018-BANE/BANE)\n- **GCN Insights**\n  - Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning, AAAI'18\n  - [[Project]](https://liqimai.github.io/blog/AAAI-18/)\n  - [[code]](https://github.com/liqimai/gcn/tree/AAAI-18/)\n- **PCTADW**\n  - Learning Embeddings of Directed Networks with Text-Associated Nodes---with Applications in Software Package Dependency Networks\n  - [[paper]](https://arxiv.org/pdf/1809.02270.pdf)\n  - [[Python]](https://github.com/shudan/PCTADW)\n  - [[dataset]](https://doi.org/10.5281/zenodo.1410669)\n- **LGCN**\n  - Large-Scale Learnable Graph Convolutional Networks, KDD'18\n  - [[paper]](http://www.kdd.org/kdd2018/accepted-papers/view/large-scale-learnable-graph-convolutional-networks)\n  - [[Python]](https://github.com/HongyangGao/LGCN)\n- **AspEm**\n  - AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks\n  - [[paper]](http://yushi2.web.engr.illinois.edu/sdm18.pdf)\n  - [[Python]](https://github.com/ysyushi/aspem)\n- **Walklets**\n  - Don't Walk, Skip! Online Learning of Multi-scale Network Embeddings\n  - [[paper]](https://arxiv.org/pdf/1605.02115.pdf)\n  - [[Python Karateclub]](https://github.com/benedekrozemberczki/karateclub)  \n  - [[Python]](https://github.com/benedekrozemberczki/walklets)  \n- **gat2vec**\n  - gat2vec: Representation learning for attributed graphs\n  - [[paper]](https://doi.org/10.1007/s00607-018-0622-9)\n  - [[Python]](https://github.com/snash4/GAT2VEC)\n- **FSCNMF**\n  - FSCNMF: Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information Networks\n  - [[paper]](https://arxiv.org/abs/1804.05313)\n  - [[Python Karateclub]](https://github.com/benedekrozemberczki/karateclub)\n  - [[Python]](https://github.com/sambaranban/FSCNMF)  \n  - [[Python]](https://github.com/benedekrozemberczki/FSCNMF)\n- **SIDE**\n  - SIDE: Representation Learning in Signed Directed Networks\n  - [[paper]](https://datalab.snu.ac.kr/side/resources/side.pdf)\n  - [[Python]](https://datalab.snu.ac.kr/side/resources/side.zip)\n  - [[Site]](https://datalab.snu.ac.kr/side/)\n- **AWE**\n  - Anonymous Walk Embeddings, ICML'18\n  - [[paper]](https://www.researchgate.net/publication/325114285_Anonymous_Walk_Embeddings)\n  - [[Python]](https://github.com/nd7141/Anonymous-Walk-Embeddings)\n- **BiNE**\n  - BiNE: Bipartite Network Embedding, SIGIR'18\n  - [[paper]](http://staff.ustc.edu.cn/~hexn/papers/sigir18-bipartiteNE.pdf)\n  - [[Python]](https://github.com/clhchtcjj/BiNE)\n- **HOPE**\n  - Asymmetric Transitivity Preserving Graph Embedding\n  - [[KDD 2016]](http://www.kdd.org/kdd2016/papers/files/rfp0184-ouA.pdf)\n  - [[Python]](https://github.com/AnryYang/HOPE)\n  - [[Python KarateClub]](https://github.com/benedekrozemberczki/karateclub) \n- **VERSE**\n  - VERSE, Versatile Graph Embeddings from Similarity Measures\n  - [[Arxiv]](https://arxiv.org/abs/1803.04742) [[WWW 2018]]\n  - [[Python]](https://github.com/xgfs/verse) \n- **AGNN**\n  - Attention-based Graph Neural Network for semi-supervised learning\n  - [[ICLR 2018 OpenReview (rejected)]](https://openreview.net/forum?id=rJg4YGWRb)\n  - [[Python]](https://github.com/dawnranger/pytorch-AGNN)\n- **SEANO**\n  - Semi-supervised Embedding in Attributed Networks with Outliers\n  - [[Paper]](https://arxiv.org/pdf/1703.08100.pdf) (SDM 2018)\n  - [[Python]](http://jiongqianliang.com/SEANO/)   \n- **Hyperbolics**\n  - Representation Tradeoffs for Hyperbolic Embeddings \n  - [[Arxiv]](https://arxiv.org/abs/1804.03329)\n  - [[Python]](https://github.com/HazyResearch/hyperbolics)   \n- **DGCNN**\n  - An End-to-End Deep Learning Architecture for Graph Classification \n  - [[AAAI 2018]](http://www.cse.wustl.edu/~muhan/papers/AAAI_2018_DGCNN.pdf)\n  - [[Lua]](https://github.com/muhanzhang/DGCNN) [[Python]](https://github.com/muhanzhang/pytorch_DGCNN)  \n- **structure2vec**\n  - Discriminative Embeddings of Latent Variable Models for Structured Data \n  - [[Arxiv]](https://arxiv.org/abs/1603.05629)\n  - [[Python]](https://github.com/Hanjun-Dai/pytorch_structure2vec)  \n- **Decagon**\n  - Decagon, Graph Neural Network for Multirelational Link Prediction \n  - [[Arxiv]](https://arxiv.org/abs/1802.00543) [[SNAP]](http://snap.stanford.edu/decagon/) [[ISMB 2018]]\n  - [[Python]](https://github.com/marinkaz/decagon)    \n- **DHNE**\n  - Structural Deep Embedding for Hyper-Networks\n  - [[AAAI 2018]](http://nrl.thumedialab.com/Structural-Deep-Embedding-for-Hyper-Networks)[[Arxiv]](https://arxiv.org/abs/1711.10146)\n  - [[Python]](https://github.com/tadpole/DHNE)  \n- **Ohmnet**\n  - Feature Learning in Multi-Layer Networks \n  - [[Arxiv]](https://arxiv.org/abs/1707.04638) [[SNAP]](http://snap.stanford.edu/ohmnet/) \n  - [[Python]](https://github.com/marinkaz/ohmnet)  \n- **SDNE**\n  - Structural Deep Network Embedding \n  - [[KDD 2016]](http://www.kdd.org/kdd2016/papers/files/rfp0191-wangAemb.pdf)\n  - [[Python]](https://github.com/xiaohan2012/sdne-keras) \n- **STWalk**\n  - STWalk: Learning Trajectory Representations in Temporal Graphs] \n  - [[Arxiv]](https://arxiv.org/abs/1711.04150)\n  - [[Python]](https://github.com/supriya-pandhre/STWalk)\n- **LoNGAE**\n  - Learning to Make Predictions on Graphs with Autoencoders \n  - [[Arxiv]](https://arxiv.org/abs/1802.08352)\n  - [[Python]](https://github.com/vuptran/graph-representation-learning)  \n- **RSDNE**\n  - [RSDNE: Exploring Relaxed Similarity and Dissimilarity from Completely-imbalanced Labels for Network Embedding.](https://zhengwang100.github.io/AAAI18_RSDNE.pdf), AAAI 2018\n  - [[Matlab]](https://github.com/zhengwang100/RSDNE) \n- **FastGCN**\n  - FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling \n  - [[Arxiv]](https://arxiv.org/abs/1801.10247), [[ICLR 2018 OpenReview]](https://openreview.net/forum?id=rytstxWAW)\n  - [[Python]](https://github.com/matenure/FastGCN)\n- **diff2vec**\n  - [Fast Sequence Based Embedding with Diffusion Graphs](http://homepages.inf.ed.ac.uk/s1668259/papers/sequence.pdf), CompleNet 2018\n  - [[Python KarateClub]](https://github.com/benedekrozemberczki/karateclub)\n  - [[Python]](https://github.com/benedekrozemberczki/diff2vec) \n- **Poincare**\n  - [Poincaré Embeddings for Learning Hierarchical Representations](https://papers.nips.cc/paper/7213-poincare-embeddings-for-learning-hierarchical-representations), NIPS 2017\n  - [[PyTorch]](https://github.com/facebookresearch/poincare-embeddings) [[Python]](https://radimrehurek.com/gensim/models/poincare.html) [[C++]](https://github.com/TatsuyaShirakawa/poincare-embedding)\n- **PEUNE**\n  - [PRUNE: Preserving Proximity and Global Ranking for Network Embedding](https://papers.nips.cc/paper/7110-prune-preserving-proximity-and-global-ranking-for-network-embedding), NIPS 2017\n  - [[code]](https://github.com/ntumslab/PRUNE)\n- **ASNE**\n  - Attributed Social Network Embedding, TKDE'18\n  - [[arxiv]](https://arxiv.org/abs/1706.01860)\n  - [[Python]](https://github.com/lizi-git/ASNE)\n  - [[Fast Python]](https://github.com/benedekrozemberczki/ASNE)\n- **GraphWave**\n  - [Spectral Graph Wavelets for Structural Role Similarity in Networks](http://snap.stanford.edu/graphwave/), \n  - [[arxiv]](https://arxiv.org/abs/1710.10321), [[ICLR 2018 OpenReview]](https://openreview.net/forum?id=rytstxWAW)\n  - [[Python]](https://github.com/snap-stanford/graphwave) [[faster version]](https://github.com/benedekrozemberczki/GraphWaveMachine)\n- **StarSpace**\n  - [StarSpace: Embed All The Things!](https://arxiv.org/pdf/1709.03856), arxiv'17\n  - [[code]](https://github.com/facebookresearch/Starspace)\n- **proNet-core**\n  - Vertex-Context Sampling for Weighted Network Embedding, arxiv'17\n  - [[arxiv]](https://arxiv.org/abs/1711.00227) [[code]](https://github.com/cnclabs/proNet-core)\n- **struc2vec**\n  - [struc2vec: Learning Node Representations from Structural Identity](https://dl.acm.org/citation.cfm?id=3098061), KDD'17\n  - [[Python]](https://github.com/leoribeiro/struc2vec)\n- **ComE**\n  - Learning Community Embedding with Community Detection and Node Embedding on Graphs, CIKM'17\n  - [[Python]](https://github.com/andompesta/ComE)\n- **BoostedNE**\n  - [Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation](https://arxiv.org/abs/1808.08627), '18\n  - [[Python KarateClub]](https://github.com/benedekrozemberczki/karateclub)\n  - [[Python]](https://github.com/benedekrozemberczki/BoostedFactorization)\n- **M-NMF**\n  - Community Preserving Network Embedding, AAAI'17\n  - [[Python TensorFlow]](https://github.com/benedekrozemberczki/M-NMF)\n  - [[Python KarateClub]](https://github.com/benedekrozemberczki/karateclub)\n- **GraphSAGE**\n  - Inductive Representation Learning on Large Graphs, NIPS'17\n  - [[arxiv]](https://arxiv.org/abs/1706.02216) [[TF]](https://github.com/williamleif/GraphSAGE) [[PyTorch]](https://github.com/williamleif/graphsage-simple/) \n- **ICE**\n  - [ICE: Item Concept Embedding via Textual Information](http://dl.acm.org/citation.cfm?id=3080807), SIGIR'17\n  - [[demo]](https://cnclabs.github.io/ICE/) [[code]](https://github.com/cnclabs/ICE)\n- **GuidedHeteEmbedding**\n  - Task-guided and path-augmented heterogeneous network embedding for author identification, WSDM'17\n  - [[paper]](https://arxiv.org/pdf/1612.02814.pdf) [[code]](https://github.com/chentingpc/GuidedHeteEmbedding)\n- **metapath2vec**\n  - metapath2vec: Scalable Representation Learning for Heterogeneous Networks, KDD'17\n  - [[paper]](https://www3.nd.edu/~dial/publications/dong2017metapath2vec.pdf) [[project website]](https://ericdongyx.github.io/metapath2vec/m2v.html)\n- **GCN**\n  - Semi-Supervised Classification with Graph Convolutional Networks, ICLR'17\n  - [[arxiv]](https://arxiv.org/abs/1609.02907)  [[Python Tensorflow]](https://github.com/tkipf/gcn)\n- **GAE**\n  - Variational Graph Auto-Encoders, arxiv\n  - [[arxiv]](https://arxiv.org/abs/1611.07308) [[Python Tensorflow]](https://github.com/tkipf/gae)\n- **CANE**\n  - CANE: Context-Aware Network Embedding for Relation Modeling, ACL'17\n  - [[paper]](http://www.thunlp.org/~tcc/publications/acl2017_cane.pdf) [[Python]](https://github.com/thunlp/cane)\n- **TransNet**\n  - TransNet: Translation-Based Network Representation Learning for Social Relation Extraction, IJCAI'17\n  - [[Python Tensorflow]](https://github.com/thunlp/TransNet)\n- **cnn_graph**\n  - Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering, NIPS'16\n  - [[Python]](https://github.com/mdeff/cnn_graph)\n- **ConvE**\n  - [Convolutional 2D Knowledge Graph Embeddings](https://arxiv.org/pdf/1707.01476v2.pdf), arxiv\n  - [[source]](https://github.com/TimDettmers/ConvE)\n- **node2vec**\n  - [node2vec: Scalable Feature Learning for Networks](http://dl.acm.org/citation.cfm?id=2939672.2939754), KDD'16\n  - [[arxiv]](https://arxiv.org/abs/1607.00653) [[Python]](https://github.com/aditya-grover/node2vec) [[Python-2]](https://github.com/apple2373/node2vec) [[Python-3]](https://github.com/eliorc/node2vec) [[C++]](https://github.com/xgfs/node2vec-c)  \n- **DNGR**\n  - [Deep Neural Networks for Learning Graph Representations](http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12423), AAAI'16\n  - [[Matlab]](https://github.com/ShelsonCao/DNGR) [[Python Keras]](https://github.com/MdAsifKhan/DNGR-Keras)\n- **HolE**\n  - [Holographic Embeddings of Knowledge Graphs](http://dl.acm.org/citation.cfm?id=3016172), AAAI'16\n  - [[Python-sklearn]](https://github.com/mnick/holographic-embeddings) [[Python-sklearn2]](https://github.com/mnick/scikit-kge)\n- **ComplEx**\n  - [Complex Embeddings for Simple Link Prediction](http://dl.acm.org/citation.cfm?id=3045609), ICML'16\n  - [[arxiv]](https://arxiv.org/abs/1606.06357) [[Python]](https://github.com/ttrouill/complex)\n- **MMDW**\n  - Max-Margin DeepWalk: Discriminative Learning of Network Representation, IJCAI'16\n  - [[paper]](http://nlp.csai.tsinghua.edu.cn/~lzy/publications/ijcai2016_mmdw.pdf)  [[Java]](https://github.com/thunlp/MMDW)\n- **planetoid**\n  - Revisiting Semi-supervised Learning with Graph Embeddings, ICML'16\n  - [[arxiv]](https://arxiv.org/abs/1603.08861) [[Python]](https://github.com/kimiyoung/planetoid)\n- **graph2vec**\n  - graph2vec: Learning Distributed Representations of Graphs, KDD'17 MLGWorkshop\n  - [[arxiv]](https://arxiv.org/abs/1707.05005)\n  - [[Python gensim]](https://github.com/benedekrozemberczki/graph2vec) [[Python KarateClub]](https://github.com/benedekrozemberczki/karateclub)\n- **PowerWalk**\n  - [PowerWalk: Scalable Personalized PageRank via Random Walks with Vertex-Centric Decomposition](http://dl.acm.org/citation.cfm?id=2983713), CIKM'16\n  - [[code]](https://github.com/lqhl/PowerWalk)\n- **LINE**\n  - [LINE: Large-scale information network embedding](http://dl.acm.org/citation.cfm?id=2741093), WWW'15\n  - [[arxiv]](https://arxiv.org/abs/1503.03578) [[C++]](https://github.com/tangjianpku/LINE) [[Python TF]](https://github.com/snowkylin/line) [[Python Theano/Keras]](https://github.com/VahidooX/LINE)\n- **PTE**\n  - [PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks](http://dl.acm.org/citation.cfm?id=2783307), KDD'15\n  - [[C++]](https://github.com/mnqu/PTE)\n- **GraRep**\n  - [Grarep: Learning graph representations with global structural information](http://dl.acm.org/citation.cfm?id=2806512), CIKM'15\n  - [[Matlab]](https://github.com/ShelsonCao/GraRep)\n  - [[Julia]](https://github.com/xgfs/GraRep.jl)\n  - [[Python]](https://github.com/benedekrozemberczki/GraRep)\n  - [[Python KarateClub]](https://github.com/benedekrozemberczki/karateclub)\n- **KB2E**\n  - [Learning Entity and Relation Embeddings for Knowledge Graph Completion](http://dl.acm.org/citation.cfm?id=2886624), AAAI'15\n  - [[paper]](http://nlp.csai.tsinghua.edu.cn/~lzy/publications/aaai2015_transr.pdf) [[C++]](https://github.com/thunlp/KB2E)  [[faster version]](https://github.com/thunlp/Fast-TransX)\n- **TADW**\n  - [Network Representation Learning with Rich Text Information](http://dl.acm.org/citation.cfm?id=2832542), IJCAI'15\n  - [[paper]](https://www.ijcai.org/Proceedings/15/Papers/299.pdf) [[Matlab]](https://github.com/thunlp/tadw) [[Python]](https://github.com/benedekrozemberczki/TADW)\n- **DeepWalk**\n  - [DeepWalk: Online Learning of Social Representations](http://dl.acm.org/citation.cfm?id=2623732), KDD'14\n  - [[arxiv]](https://arxiv.org/abs/1403.6652) [[Python]](https://github.com/phanein/deepwalk)  [[C++]](https://github.com/xgfs/deepwalk-c)\n- **GEM**\n  - Graph Embedding Techniques, Applications, and Performance: A Survey\n  - [[arxiv]](https://arxiv.org/abs/1705.02801) [[Python]](https://github.com/palash1992/GEM)\n- **DNE-SBP**\n  - Deep Network Embedding for Graph Representation Learning in Signed Networks\n  - [[paper]](https://ieeexplore.ieee.org/document/8486671) [[Code]](https://github.com/shenxiaocam/Deep-network-embedding-for-graph-representation-learning-in-signed-networks)\n\n# Paper References\n\n[A Comprehensive Survey on Graph Neural Networks](https://arxiv.org/abs/1901.00596), arxiv'19\n\n[Hierarchical Graph Representation Learning with Differentiable Pooling](https://arxiv.org/pdf/1806.08804.pdf), NIPS'18\n\n**SEMAC**, [Link Prediction via Subgraph Embedding-Based Convex Matrix Completion](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16442), AAAI 2018, [Slides](https://www.slideshare.net/gdm3003/semac-graph-node-embeddings-for-link-prediction)\n\n**MILE**, [MILE: A Multi-Level Framework for Scalable Graph Embedding](https://arxiv.org/pdf/1802.09612.pdf), arxiv'18\n\n**MetaGraph2Vec**, [MetaGraph2Vec: Complex Semantic Path Augmented Heterogeneous Network Embedding](https://arxiv.org/abs/1803.02533)\n\n**PinSAGE**, [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://arxiv.org/abs/1806.01973)\n\n[Curriculum Learning for Heterogeneous Star Network Embedding via Deep Reinforcement Learning](https://dl.acm.org/citation.cfm?id=3159711), WSDM '18\n\n[Adversarial Network Embedding](https://arxiv.org/abs/1711.07838), arxiv\n\n**Role2Vec**, [Learning Role-based Graph Embeddings](https://arxiv.org/abs/1802.02896)\n\n**edge2vec**, [Feature Propagation on Graph: A New Perspective to Graph Representation\nLearning](https://arxiv.org/abs/1804.06111)\n\n**MINES**, [Multi-Dimensional Network Embedding with Hierarchical Structure](http://cse.msu.edu/~mayao4/downloads/Multidimensional_Network_Embedding_with_Hierarchical_Structure.pdf)\n\n[Walk-Steered Convolution for Graph Classification](https://arxiv.org/abs/1804.05837)\n\n[Deep Feature Learning for Graphs](https://arxiv.org/abs/1704.08829), arxiv'17\n\n[Fast Linear Model for Knowledge Graph Embeddings](https://arxiv.org/abs/1710.10881), arxiv'17\n\n[Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec](https://arxiv.org/abs/1710.02971), arxiv'17\n\n[A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://arxiv.org/abs/1709.07604), arxiv'17\n\n[Representation Learning on Graphs: Methods and Applications](https://arxiv.org/pdf/1709.05584.pdf), IEEE DEB'17\n\n**CONE**, [CONE: Community Oriented Network Embedding](https://arxiv.org/abs/1709.01554), arxiv'17\n\n**LANE**, \n[Label Informed Attributed Network Embedding](http://dl.acm.org/citation.cfm?id=3018667), WSDM'17\n\n**Graph2Gauss**,\n[Deep Gaussian Embedding of Attributed Graphs: Unsupervised Inductive Learning via Ranking](https://arxiv.org/abs/1707.03815), arxiv\n[[Bonus Animation]](https://twitter.com/abojchevski/status/885502050133585925)\n\n[Scalable Graph Embedding for Asymmetric Proximity](https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14696), AAAI'17\n\n[Query-based Music Recommendations via Preference Embedding](http://dl.acm.org/citation.cfm?id=2959169), RecSys'16\n\n[Tri-party deep network representation](http://dl.acm.org/citation.cfm?id=3060886), IJCAI'16\n\n[Heterogeneous Network Embedding via Deep Architectures](http://dl.acm.org/citation.cfm?id=2783296), KDD'15\n\n[Neural Word Embedding As Implicit Matrix Factorization](http://dl.acm.org/citation.cfm?id=2969070), NIPS'14\n\n[Distributed large-scale natural graph factorization](http://dl.acm.org/citation.cfm?id=2488393), WWW'13\n\n[From Node Embedding To Community Embedding](https://arxiv.org/abs/1610.09950), arxiv\n\n[Walklets: Multiscale Graph Embeddings for Interpretable Network Classification](https://arxiv.org/abs/1605.02115), arxiv\n\n[Comprehend DeepWalk as Matrix Factorization](https://arxiv.org/abs/1501.00358), arxiv\n\n# Conference \u0026 Workshop\n\n[Graph Neural Networks for Natural Language Processing](https://github.com/svjan5/GNNs-for-NLP), **EMNLP'19**\n\n[SMORe : Modularize Graph Embedding for Recommendation](https://github.com/cnclabs/smore), **RecSys'19**\n\n[13th International Workshop on Mining and Learning with Graphs](http://www.mlgworkshop.org/2017/), **MLG'17**\n\n[WWW-18 Tutorial Representation Learning on Networks](http://snap.stanford.edu/proj/embeddings-www/), **WWW'18**\n\n# Related List\n\n[awesome-graph-classification](https://github.com/benedekrozemberczki/awesome-graph-classification)\n\n[awesome-community-detection](https://github.com/benedekrozemberczki/awesome-community-detection)\n\n[awesome-embedding-models](https://github.com/Hironsan/awesome-embedding-models)\n\n[Must-read papers on network representation learning (NRL) / network embedding (NE)](https://github.com/thunlp/NRLPapers)\n\n[Must-read papers on knowledge representation learning (KRL) / knowledge embedding (KE)](https://github.com/thunlp/KRLPapers)\n\n[Network Embedding Resources](https://github.com/nate-russell/Network-Embedding-Resources)\n\n[awesome-embedding-models](https://github.com/Hironsan/awesome-embedding-models)\n\n[2vec-type embedding models](https://github.com/MaxwellRebo/awesome-2vec)\n\n[Must-read papers on GNN](https://github.com/thunlp/GNNPapers)\n\n[LiteratureDL4Graph](https://github.com/DeepGraphLearning/LiteratureDL4Graph)\n\n[awesome-graph-classification](https://github.com/benedekrozemberczki/awesome-graph-classification)\n\n# Related Project\n\n**Stanford Network Analysis Project** [website](http://snap.stanford.edu/)\n\n**StellarGraph Machine Learning Library** [website](https://www.stellargraph.io) [GitHub](https://github.com/stellargraph/stellargraph)\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/chihming%2Fawesome-network-embedding/projects"}