awesome-network-embedding
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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 Github: https://github.com/chihming/awesome-network-embedding