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:)
- AmpliGraph
- jodie
- PyTorch-BigGraph
- Pytorch-BigGraph - a distributed system for learning graph embeddings for large graphs, SysML'19
- [github]
- ATP
- MUSAE
- SEAL-CI
- Semi-Supervised Graph Classification: A Hierarchical Graph Perspective, WWW'19
- [paper]
- [Python PyTorch]
- N-GCN
- 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
- 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
- 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 PyTorch]
- [C++]
- SGCN
- TENE
- DANMF
- BANE
- GCN Insights
- PCTADW
- LGCN
- AspEm
- Walklets
- gat2vec
- FSCNMF
- SIDE
- AWE
- BiNE
- HOPE
- Asymmetric Transitivity Preserving Graph Embedding
- [KDD 2016]
- [Python]
- VERSE
- AGNN
- Attention-based Graph Neural Network for semi-supervised learning
- [ICLR 2018 OpenReview (rejected)]
- [Python]
- SEANO
- Hyperbolics
- DGCNN
- An End-to-End Deep Learning Architecture for Graph Classification
- [AAAI 2018]
- [Lua] [Python]
- structure2vec
- Decagon
- DHNE
- Structural Deep Embedding for Hyper-Networks
- [AAAI 2018][Arxiv]
- [Python]
- Ohmnet
- SDNE
- Structural Deep Network Embedding
- [KDD 2016]
- [Python]
- STWalk
- LoNGAE
- RSDNE
- 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
- PEUNE
- ASNE
- Attributed Social Network Embedding, arxiv'17
- [arxiv]
- [Python]
- [Fast Python]
- GraphWave
- StarSpace
- StarSpace: Embed All The Things!, arxiv'17
- [code]
- proNet-core
- struc2vec
- ComE
- Learning Community Embedding with Community Detection and Node Embedding on Graphs, CIKM'17
- [Python]
- BoostedNE
- M-NMF
- Community Preserving Network Embedding, AAAI'17
- [Python]
- GraphSAGE
- ICE
- GuidedHeteEmbedding
- 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
- 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
- node2vec
- DNGR
- HolE
- ComplEx
- MMDW
- planetoid
- graph2vec
- PowerWalk
- LINE
- PTE
- GraRep
- KB2E
- TADW
- DeepWalk
- GEM
- DNE-SBP
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
13th International Workshop on Mining and Learning with Graphs, MLG'17
WWW-18 Tutorial Representation Learning on Networks, WWW'18
Must-read papers on network representation learning (NRL) / network embedding (NE)
Must-read papers on knowledge representation learning (KRL) / knowledge embedding (KE)
Stanford Network Analysis Project website