- The Graph Neural Network Model, [Link]
- Geometric deep learning: going beyond Euclidean data, [Link], [YouTube]
- Relational inductive biases, deep learning, and graph networks, [Link]
- 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]
- 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]
- Hyperbolic Graph Convolutional Neural Networks, [Link]
- Hyperbolic Graph Neural Networks, [Link], [GitHub]
- Variational Graph Auto-Encoders, [Link], [Github]
- Modeling Relational Data with Graph Convolutional Networks, [Link], [GitHub]
- Graph Convolutional Matrix Completion, [Link], [GitHub]
- SchNet
- MoleculeNet
- Bayesian GCN
- Ineraction Networks
- Neural Relational Inference
- Symbolic Physics
- H-OGN
- SRNN