Here, you’ll find a few resources that’ll get you started with Graph Neural Networks. I will keep updating it so you can always check back!.
Intro to graph representation learning
- Representation learning on graphs: Methods and Applications; Hamilton et al.
- Graph Neural Networks: A review or methods and applications; Zhou et al;
- Deep Learning on graphs: A survey; Ziwei Zhang et al
- Graph Representation learning book; Will Hamilton (Highly recommended for comprehensive understanding)
- Geometric Deep Learning Grids, Groups, Graphs, Geodesics, and Gauges; Bronstein et al
Important to know architectures
- Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering (ChebNet); Deferrard et al
- Graph Convolutional Neural Networks; Kipf et al.
- Graph Attention Networks (GAT); Velickovic et al.
- Inductive Representation learning in large graphs; Hamilton et al
- Graph Isormorphism Networks; Xu et al.
- Gated Graph Sequence Neural Networks; Li et al
- Benchmarking Graph Neural Networks; Dwivedi et al
- PyTorch Geometric (Based on pytorch)
- Spekral (Tensorflow/ keras)
- Deep Graph Library (Framework agnostic, compatible with PyTorch, Tensorflow, MxNet etc)
- Open Graph Benchmark: Datasets for Machine Learning on graphs