awesome-network-embedding

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]
  • [Python]
• FEATHER
  • Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models, CIKM 2020
  • [Paper]
  • [Python]
  • [Python KarateClub]
• HeGAN
  • Adversarial Learning on Heterogeneous Information Networks, KDD 2019
  • [Paper]
  • [Python]
• NetMF
  • Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and Node2Vec, WSDM 2018
  • [Paper]
  • [Python KarateClub]
• GL2Vec
  • GL2vec: Graph Embedding Enriched by Line Graphs with Edge Features, ICONIP 2019
  • [Paper]
  • [Python KarateClub]
• NNSED
  • A Non-negative Symmetric Encoder-Decoder Approach for Community Detection, CIKM 2017
  • [Paper]
  • [Python KarateClub]
• SymmNMF
  • Symmetric Nonnegative Matrix Factorization for Graph Clustering, SDM 2012
  • [Paper]
  • [Python KarateClub]
• RECT
  • Network Embedding with Completely-Imbalanced Labels, TKDE 2020
  • [Paper]
  • [Python]
• GEMSEC
  • GEMSEC: Graph Embedding with Self Clustering, ASONAM 2019
  • [Paper]
  • [Python]
• AmpliGraph
  • Library for learning knowledge graph embeddings with TensorFlow
  • [Project]
  • [code]
• jodie
  • Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks, KDD’19
  • [Project]
  • [Code]
• PyTorch-BigGraph
  • Pytorch-BigGraph - a distributed system for learning graph embeddings for large graphs, SysML’19
  • [github]
• ATP
  • ATP: Directed Graph Embedding with Asymmetric Transitivity Preservation, AAAI’19
  • [paper]
  • [code]
• MUSAE
  • Multi-scale Attributed Node Embedding, ArXiv 2019
  • [paper]
  • [Python KarateClub]
  • [Python]
• SEAL-CI
  • Semi-Supervised Graph Classification: A Hierarchical Graph Perspective, WWW’19
  • [paper]
  • [Python PyTorch]
• N-GCN and MixHop
  • A Higher-Order Graph Convolutional Layer, NIPS’18 (workshop)
  • [paper]
  • [Python PyTorch]
• CapsGNN
  • Capsule Graph Neural Network, ICLR’19
  • [paper]
  • [Python PyTorch]
• Splitter
  • Splitter: Learning Node Representations that Capture Multiple Social Contexts, WWW’19
  • [paper]
  • [Python PyTorch]
• REGAL
  • REGAL: Representation Learning-based Graph Alignment. International Conference on Information and Knowledge Management, CIKM’18
  • [arxiv]
  • [paper]
  • [code]
• PyTorch Geometric
  • Fast Graph Representation Learning With PyTorch Geometric
  • [paper]
  • [Python PyTorch]
• TuckER
  • Tensor Factorization for Knowledge Graph Completion, Arxiv’19
  • [paper]
  • [Python PyTorch]
• HypER
  • Hypernetwork Knowledge Graph Embeddings, Arxiv’18
  • [paper]
  • [Python PyTorch]
• GWNN
  • Graph Wavelet Neural Network, ICLR’19
  • [paper]
  • [Python PyTorch]
  • [Python TensorFlow]
• APPNP
  • Combining Neural Networks with Personalized PageRank for Classification on Graphs, ICLR’19
  • [paper]
  • [Python PyTorch]
  • [Python TensorFlow]
• role2vec
  • Learning Role-based Graph Embeddings, IJCAI’18
  • [paper]
  • [Python KarateClub]
  • [Python]
• AttentionWalk
  • Watch Your Step: Learning Node Embeddings via Graph Attention, NIPS’18
  • [paper]
  • [Python]
  • [Python PyTorch]
  • [Python TensorFlow]
• GAT
  • Graph Attention Networks, ICLR’18
  • [paper]
  • [Python PyTorch]
  • [Python TensorFlow]
• SINE
  • SINE: Scalable Incomplete Network Embedding, ICDM’18
  • [paper]
  • [Python KarateClub]
  • [Python PyTorch]
  • [C++]
• SGCN
  • Signed Graph Convolutional Network, ICDM’18
  • [paper]
  • [Python]
• TENE
  • Enhanced Network Embedding with Text Information, ICPR’18
  • [paper]
  • [Python KarateClub]
  • [Python]
• DANMF
  • Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection, CIKM’18
  • [paper]
  • [Python KarateClub]
  • [Python]
  • [Matlab]
    
• BANE
  • Binarized Attributed Network Embedding, ICDM’18
  • [paper]
  • [Python KarateClub]
  • [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 Karateclub]
    
  • [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 Karateclub]
  • [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]
  • [Python KarateClub]
• 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]
    
• DHNE
  • Structural Deep Embedding for Hyper-Networks
  • [AAAI 2018][Arxiv]
  • [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]
• diff2vec
  • Fast Sequence Based Embedding with Diffusion Graphs, CompleNet 2018
  • [Python KarateClub]
  • [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, TKDE’18
  • [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 KarateClub]
  • [Python]
• M-NMF
  • Community Preserving Network Embedding, AAAI’17
  • [Python TensorFlow]
  • [Python KarateClub]
• GraphSAGE
  • Inductive Representation Learning on Large Graphs, NIPS’17
  • [arxiv] [TF] [PyTorch]
• ICE
  • ICE: Item Concept Embedding via Textual Information, SIGIR’17
  • [demo] [code]
• GuidedHeteEmbedding
  • Task-guided and path-augmented heterogeneous network embedding for author identification, WSDM’17
  • [paper] [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 gensim] [Python KarateClub]
• 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]
  • [Julia]
  • [Python]
  • [Python KarateClub]
• 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]
• DNE-SBP
  • Deep Network Embedding for Graph Representation Learning in Signed Networks
  • [paper] [Code]

Paper References

A Comprehensive Survey on Graph Neural Networks, arxiv’19

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

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

Graph Neural Networks for Natural Language Processing, EMNLP’19

SMORe : Modularize Graph Embedding for Recommendation, RecSys’19

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

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

Related List

awesome-graph-classification

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

Must-read papers on GNN

LiteratureDL4Graph

awesome-graph-classification

Related Project

Stanford Network Analysis Project website

StellarGraph Machine Learning Library website GitHub