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)

• DANMF

  • Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection, CIKM’18
  • [paper]
  • [Python]
  • [Matlab]

• BANE

  • Binarized Attributed Network Embedding, ICDM’18
  • [paper]
  • [Python]
  • [Matlab]

• GCN Insights

  • Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning, AAAI’18
  • [Project]
  • [code]

• PCTADW

  • Learning Embeddings of Directed Networks with Text-Associated Nodes—with Applications in Software Package Dependency Networks
  • [paper]
  • [Python]
  • [dataset]

• LGCN

  • Large-Scale Learnable Graph Convolutional Networks, KDD’18
  • [paper]
  • [Python]

• AspEm

  • AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks
  • [paper]
  • [Python]

• Walklets

  • Don’t Walk, Skip! Online Learning of Multi-scale Network Embeddings
  • [paper]
  • [Python]

• gat2vec

  • gat2vec: Representation learning for attributed graphs
  • [paper]
  • [Python]

• FSCNMF

  • FSCNMF: Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information Networks
  • [paper]
  • [Python]
  • [Python]

• SIDE

  • SIDE: Representation Learning in Signed Directed Networks
  • [paper]
  • [Python]
  • [Site]

• AWE

  • Anonymous Walk Embeddings, ICML’18
  • [paper]
  • [Python]

• BiNE

  • BiNE: Bipartite Network Embedding, SIGIR’18
  • [paper]
  • [Python]

• HOPE

  • Asymmetric Transitivity Preserving Graph Embedding
  • [KDD 2016]
  • [Python]

• VERSE

  • VERSE, Versatile Graph Embeddings from Similarity Measures
  • [Arxiv] [[WWW 2018]]
  • [Python]

• AGNN

  • Attention-based Graph Neural Network for semi-supervised learning
  • [ICLR 2018 OpenReview (rejected)]
  • [Python]

• SEANO

  • Semi-supervised Embedding in Attributed Networks with Outliers
  • [Paper] (SDM 2018)
  • [Python]

• Hyperbolics

  • Representation Tradeoffs for Hyperbolic Embeddings
  • [Arxiv]
  • [Python]

• DGCNN

  • An End-to-End Deep Learning Architecture for Graph Classification
  • [AAAI 2018]
  • [Lua] [Python]

• structure2vec

  • Discriminative Embeddings of Latent Variable Models for Structured Data
  • [Arxiv]
  • [Python]

• Decagon

  • Decagon, Graph Neural Network for Multirelational Link Prediction
  • [Arxiv] [SNAP] [[ISMB 2018]]
  • [Python]

• Ohmnet

  • Feature Learning in Multi-Layer Networks
  • [Arxiv] [SNAP]
  • [Python]

• SDNE

  • Structural Deep Network Embedding
  • [KDD 2016]
  • [Python]

• STWalk

  • STWalk: Learning Trajectory Representations in Temporal Graphs]
  • [Arxiv]
  • [Python]

• LoNGAE

  • Learning to Make Predictions on Graphs with Autoencoders
  • [Arxiv]
  • [Python]

• RSDNE

  • RSDNE: Exploring Relaxed Similarity and Dissimilarity from Completely-imbalanced Labels for Network Embedding., AAAI 2018
  • [Matlab]

• FastGCN

  • FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling
  • [Arxiv], [ICLR 2018 OpenReview]
  • [Python]

• GEMSEC

  • GEMSEC: Graph Embedding with Self Clustering, arXiv 2018
  • [Python]

• diff2vec

  • Fast Sequence Based Embedding with Diffusion Graphs, CompleNet 2018
  • [Python]

• Poincare

  • Poincaré Embeddings for Learning Hierarchical Representations, NIPS 2017
  • [PyTorch] [Python] [C++]

• PEUNE

  • PRUNE: Preserving Proximity and Global Ranking for Network Embedding, NIPS 2017
  • [code]

• ASNE

  • Attributed Social Network Embedding, arxiv’17
  • [arxiv]
  • [Python]
  • [Fast Python]

• GraphWave

  • Spectral Graph Wavelets for Structural Role Similarity in Networks,
  • [arxiv], [ICLR 2018 OpenReview]
  • [Python] [faster version]

• StarSpace

  • StarSpace: Embed All The Things!, arxiv’17
  • [code]

• proNet-core

  • Vertex-Context Sampling for Weighted Network Embedding, arxiv’17
  • [arxiv] [code]

• struc2vec

  • struc2vec: Learning Node Representations from Structural Identity, KDD’17
  • [Python]

• ComE

  • Learning Community Embedding with Community Detection and Node Embedding on Graphs, CIKM’17
  • [Python]

• BoostedNE

  • Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation, ’18
  • [Python]

• M-NMF

  • Community Preserving Network Embedding, AAAI’17
  • [Python]

• GraphSAGE

  • Inductive Representation Learning on Large Graphs, NIPS’17
  • [arxiv] [TF] [PyTorch]

• ICE

  • ICE: Item Concept Embedding via Textual Information, SIGIR’17
  • [demo] [code]

• metapath2vec

  • metapath2vec: Scalable Representation Learning for Heterogeneous Networks, KDD’17
  • [paper] [project website]

• GCN

  • Semi-Supervised Classification with Graph Convolutional Networks, ICLR’17
  • [arxiv] [Python Tensorflow]

• GAE

  • Variational Graph Auto-Encoders, arxiv
  • [arxiv] [Python Tensorflow]

• CANE

  • CANE: Context-Aware Network Embedding for Relation Modeling, ACL’17
  • [paper] [Python]

• TransNet

  • TransNet: Translation-Based Network Representation Learning for Social Relation Extraction, IJCAI’17
  • [Python Tensorflow]

• cnn_graph

  • Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering, NIPS’16
  • [Python]

• ConvE

  • Convolutional 2D Knowledge Graph Embeddings, arxiv
  • [source]

• node2vec

  • node2vec: Scalable Feature Learning for Networks, KDD’16
  • [arxiv] [Python] [Python-2] [Python-3] [C++]

• DNGR

  • Deep Neural Networks for Learning Graph Representations, AAAI’16
  • [Matlab] [Python Keras]

• HolE

  • Holographic Embeddings of Knowledge Graphs, AAAI’16
  • [Python-sklearn] [Python-sklearn2]

• ComplEx

  • Complex Embeddings for Simple Link Prediction, ICML’16
  • [arxiv] [Python]

• MMDW

  • Max-Margin DeepWalk: Discriminative Learning of Network Representation, IJCAI’16
  • [paper] [Java]

• planetoid

  • Revisiting Semi-supervised Learning with Graph Embeddings, ICML’16
  • [arxiv] [Python]

• graph2vec

  • graph2vec: Learning Distributed Representations of Graphs, KDD’17 MLGWorkshop
  • [arxiv] [Python]

• PowerWalk

  • PowerWalk: Scalable Personalized PageRank via Random Walks with Vertex-Centric Decomposition, CIKM’16
  • [code]

• LINE

  • LINE: Large-scale information network embedding, WWW’15
  • [arxiv] [C++] [Python TF] [Python Theano/Keras]

• PTE

  • PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks, KDD’15
  • [C++]

• GraRep

  • Grarep: Learning graph representations with global structural information, CIKM’15
  • [Matlab]

• KB2E

  • Learning Entity and Relation Embeddings for Knowledge Graph Completion, AAAI’15
  • [paper] [C++] [faster version]

• TADW

  • Network Representation Learning with Rich Text Information, IJCAI’15
  • [paper] [Matlab] [Python]

• DeepWalk

  • DeepWalk: Online Learning of Social Representations, KDD’14
  • [arxiv] [Python] [C++]

• GEM

  • Graph Embedding Techniques, Applications, and Performance: A Survey
  • [arxiv] [Python]

Paper References

Hierarchical Graph Representation Learning with Differentiable Pooling, NIPS’18

SEMAC, Link Prediction via Subgraph Embedding-Based Convex Matrix Completion, AAAI 2018, Slides

MILE, MILE: A Multi-Level Framework for Scalable Graph Embedding, arxiv’18

MetaGraph2Vec, MetaGraph2Vec: Complex Semantic Path Augmented Heterogeneous Network Embedding

PinSAGE, Graph Convolutional Neural Networks for Web-Scale Recommender Systems

Curriculum Learning for Heterogeneous Star Network Embedding via Deep Reinforcement Learning, WSDM ’18

Adversarial Network Embedding, arxiv

Role2Vec, Learning Role-based Graph Embeddings

edge2vec, Feature Propagation on Graph: A New Perspective to Graph Representation Learning

MINES, Multi-Dimensional Network Embedding with Hierarchical Structure

Walk-Steered Convolution for Graph Classification

Deep Feature Learning for Graphs, arxiv’17

Watch Your Step: Learning Graph Embeddings Through Attention, arxiv’17

Fast Linear Model for Knowledge Graph Embeddings, arxiv’17

Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec, arxiv’17

A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications, arxiv’17

Representation Learning on Graphs: Methods and Applications, IEEE DEB’17

CONE, CONE: Community Oriented Network Embedding, arxiv’17

LANE, Label Informed Attributed Network Embedding, WSDM’17

Graph2Gauss, Deep Gaussian Embedding of Attributed Graphs: Unsupervised Inductive Learning via Ranking, arxiv [Bonus Animation]

Scalable Graph Embedding for Asymmetric Proximity, AAAI’17

Query-based Music Recommendations via Preference Embedding, RecSys’16

Tri-party deep network representation, IJCAI’16

Heterogeneous Network Embedding via Deep Architectures, KDD’15

Neural Word Embedding As Implicit Matrix Factorization, NIPS’14

Distributed large-scale natural graph factorization, WWW’13

From Node Embedding To Community Embedding, arxiv

Walklets: Multiscale Graph Embeddings for Interpretable Network Classification, arxiv

Comprehend DeepWalk as Matrix Factorization, arxiv

Conference & Workshop

13th International Workshop on Mining and Learning with Graphs, MLG’17

WWW-18 Tutorial Representation Learning on Networks, WWW’18

Related List

awesome-graph-embedding

awesome-community-detection

awesome-embedding-models

Must-read papers on network representation learning (NRL) / network embedding (NE)

Must-read papers on knowledge representation learning (KRL) / knowledge embedding (KE)

Network Embedding Resources

awesome-embedding-models

2vec-type embedding models

Related Project

Stanford Network Analysis Project website