GNN Lectures

• CS224W by Jure Leskovec, [Link]

GNN frameworks

• PyTorch Geometric, [Doc]
• TensorFlow GraphNets, [GitHub]

GNN architectures

Review papers

• The Graph Neural Network Model, [Link]
• Geometric deep learning: going beyond Euclidean data, [Link], [YouTube]
• Relational inductive biases, deep learning, and graph networks, [Link]

Important papers

• Convolutional Networks on Graphs for Learning Molecular Fingerprints, [Link], [GitHub]
• Gated Graph Sequence Neural Networks, [Link]
• Semi-Supervised Classification with Graph Convolutional Networks, [Link], [GitHub]
• Neural Message Passing for Quantum Chemistry, [Link], [GitHub]
• Graph Attention Networks, [Link], [GitHub]
• How Powerful are Graph Neural Networks?, [Link]

Graph generative models

• Learning Deep Generative Models of Graphs, [Link], [Github]
• MolGAN: An implicit generative model for small molecular graphs, [Link], [GitHub]
• Junction Tree Variational Autoencoder for Molecular Graph Generation, [Link], [GitHub]
• Constrained Graph Variational Autoencoders for Molecule Design, [Link], [GitHub]
• Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation, [Link], [GitHub]
• DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation, [Link],
• Learning Multimodal Graph-to-Graph Translation for Molecular Optimization, [Link], [GitHub]
• Efficient Graph Generation with Graph Recurrent Attention Networks, [Link], [GitHub]
• Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation, [Link], [GitHub]

Pooling methods

• Self-Attention Graph Pooling, [Link], [GitHub]

Node/Graph classification

• Hyperbolic Graph Convolutional Neural Networks, [Link]
• Hyperbolic Graph Neural Networks, [Link], [GitHub]

Link prediction and recommender system

• Variational Graph Auto-Encoders, [Link], [Github]
• Modeling Relational Data with Graph Convolutional Networks, [Link], [GitHub]
• Graph Convolutional Matrix Completion, [Link], [GitHub]

Molecular applications

• SchNet: A continuous-filter convolutional neural network for modeling quantum interactions, [Link]
• MoleculeNet: a benchmark for molecular machine learning, [Link]
• Bayesian graph convolutional neural networks for semi-supervised classification, [Link]

Physics modeling

• Interaction Networks for Learning about Objects, Relations and Physics, [Link]
• Neural Relational Inference for Interacting Systems, [Link]
• Learning Symbolic Physics with Graph Networks, [Link]
• Structural Recurrent Neural Network (SRNN) for Group Activity Analysis, [Link]
• H-OGN

Refrence

https://github.com/GNN-KR/paper_list