All materials related to GNN
- Deep Learning on Graphs: A Survey, arXiv 2018
- A Comprehensive Survey on Graph Neural Networks,arXiv 2018
- Graph Neural Networks: A Review of Methods and Applications,arXiv 2018
- Relational inductive biases, deep learning, and graph networks,arXiv 2018
- The first motivation of GNNs roots in convolutional neural networks (CNNs)
- The other motivation comes from graph embedding, which learns to represent graph nodes, edges or subgraphs in low-dimensional vectors.
- GNNs propagate on each node respectively, ignoring the input order of nodes
- GNNs can do propagation guided by the graph structure instead of using it as part of features
- GNNs explore to generate the graph from non structural data like scene pictures and story documents, which can be a powerful neural model for further high-level AI.
- Irregular domain
- Varying structures and tasks
- Scalability and parallelization
- Interdiscipline
- Message Passing Neural Networks(MPNN)
- Non-local Neural Networks(NLNN)
- Graph Networks(GN)
- Graph Neural Networks
- Graph Convolutional Networks
- Spectral-based
- Spatial-based
- Pooling modules
- Graph Auto-encoders
- Auto-encoders
- Variational Auto-encoders
- Graph Attention Networks
- Graph Generative Networks
- Graph Spatial-Temporal Networks
- Graph Recurrent Neural Networks
- Graph Reinforcement Learning
- Citation Networks
- Cora (Collective classification in network data,AI magazine,2008)
- Citeseer (Collective classification in network data,AI magazine,2008)
- Pubmed (Collective classification in network data,AI magazine,2008)
- DBLP
- Social Networks
- BlogCatalog (Relational learning via latent social dimensions,KDD 2009)
- Reddit (representation learning on large graphs,NIPS 2017)
- Epinions
- Chemical/Biological Graphs
- PPI (Predicting multicellular function through multi-layer tissue networks,Bioinformatics 2017)
- NCI-1 (Comparison of descriptor spaces for chemical compound retrieval and classification,KIS 2008)
- NCI-109 (Comparison of descriptor spaces for chemical compound retrieval and classification,KIS 2008)
- MUTAG (Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity,Journal of medicinal chemistry,1991)
- D&D (Distinguishing enzyme structures from non-enzymes without alignments,Journal of molecular biology 2003)
- QM9 (Quantum chemistry structures and properties of 134 kilo molecules,Scientific data 2014)
- tox21
- Unstructured Graphs
- Others
- METR-LA (Big data and its technical challenges,Communications of the ACM 2014)
- Movie-Lens1M
- Nell (Toward an architecture for never-ending language learning,AAAI 2010)
- ChebNet
- 1stChebNet
- GGNNs
- SSE
- GraphSage
- LGCN
- SplineCNN
- GAT
- GAE
- ARGA
- DNGR
- SDNE
- DRNE
- GraphRNN
- DCRNN
- CNN-GCN
- ST-GCN
- Structural RNN
- Bilinear GNN
- PyGAS: Auto-Scaling GNNs in PyG
- Soft-mask: Adaptive Substructure Extractions for Graph Neural Networks
- IVGD: Invertible Validity-aware Graph Diffusion
- Graph Attention Multi-Layer Perceptron
- Structured Variational Graph Autoencoder
- GTaxoGym: Taxonomy of Benchmarks in Graph Representation Learning
- A Survey of Deep Graph Clustering: Taxonomy, Challenge, and Application
- A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking
- AliGraph: A comprehensive graph neural network platform
- DistDGL: Distributed graph neural network training for billion-scale graphs
- DGL is an easy-to-use, high performance and scalable Python package for deep learning on graphs
- traffic forecasting
- TrafficStream: A Streaming Traffic Flow Forecasting FrameworkBased on Graph Neural Networks and Continual Learning（IJCAI 2021)
- Spatial-Temporal Graph ODE Neural Network(2021 KDD)
- Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting
- MegaCRN: Meta-Graph Convolutional Recurrent Network
- DAAGCN: Dynamic Adaptive and Adversarial Graph Convolutional Network for Traffic Forecasting
- Combined Dynamic Virtual Spatiotemporal Graph Mapping for Traffic Prediction
- FOGS: First-Order Gradient Supervision with Learning-based Graph for Traffic Flow Forecasting
- Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic Prediction
- ST-SSL: Spatio-Temporal Self-Supervised Learning for Traffic Prediction
- recommendation
- differential privacy
- link prediction
- interpretability
- source localization
- Calibration
- Transformer
- Different types of graphs
- Dynamic graphs
- Interpretability
- Compositionality
- Go Deep
- Receptive Field
- Scalability
- Shallow Structure(graph neural net works are always shallow, most of which are no more than three layers.)
- Non-Structural Scenarios
- Green deep learning
- Low resource learning(FSL and ZSL)
- Awesome Graph Neural Networks
- GNNPaper:Must-read papers 清华大学NLP组
- GNN相关的一些论文以及最新进展
- Literature of Deep Learning for Graphs
- Graph-based deep learning literature
- spatio temporal-paper-list(graph convolutional)
- Python package built to ease deep learning on graph, on top of existing DL frameworks
- 对于GNN综述文章的一个整理
- Geometric Deep Learning Extension Library for PyTorch
- 关于GNN的pytorch模型示例
- Graph Neural Networks for Natural Language Processing
- Graph Neural Network for Traffic Forecasting
- self-supervised learning on Graph Neural Networks
- awesome auto graph learning
- A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications
- Reinforcement learning on graphs: A survey
- A Python Library for Graph Outlier Detection (Anomaly Detection)
- A Python Library for Graph Outlier Detection
- Data Mining
- CV