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