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# 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:)
<img src="NE.png" width="480">
# 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
)
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