graph NNs: 8 curated resources
graph NNs
8 resources · page 1 of 1
DGL
by DGL team
Deep Graph Library (DGL): framework for deep learning on graphs, with docs and examples.
Geometric Deep Learning (Bronstein et al.)
by Michael Bronstein, Joan Bruna, Taco Cohen and Petar Veličković
Geometric Deep Learning site (Bronstein et al.): proto-book and lectures on grids, groups, graphs and geodesics.
Graph Network-based Simulators
DeepMind learning_to_simulate code on GitHub: graph network-based simulators for particle physics.
PyTorch Geometric
PyTorch Geometric (PyG) documentation: library for building and training graph neural networks.
GAT
by Petar Veličković et al. (University of Cambridge)
arXiv paper (2017) "Graph Attention Networks", introducing GAT, which weights neighbour nodes with masked self-attention.
GCN
by Thomas N. Kipf and Max Welling (University of Amsterdam)
arXiv paper (2016) "Semi-Supervised Classification with Graph Convolutional Networks", introducing GCN layers for node classification.
GraphSAGE
by William L. Hamilton et al. (Stanford University)
arXiv paper (2017) "Inductive Representation Learning on Large Graphs", introducing GraphSAGE for inductive node embeddings.
Relational inductive biases, deep learning, and graph networks
by Peter W. Battaglia et al. (DeepMind)
arXiv paper (2018) "Relational inductive biases, deep learning, and graph networks", defining the graph network block that unifies many GNN variants.