Getting Started with Graph Neural Networks

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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

  1. Representation learning on graphs: Methods and Applications; Hamilton et al.
  2. Graph Neural Networks: A review or methods and applications; Zhou et al;
  3. Deep Learning on graphs: A survey; Ziwei Zhang et al


  1. Graph Representation learning book; Will Hamilton (Highly recommended for comprehensive understanding)
  2. Geometric Deep Learning Grids, Groups, Graphs, Geodesics, and Gauges; Bronstein et al


  1. Machine Learning with Graphs; Jure Leskovec (Stanford University), Videos, Course website

Important to know architectures

  1. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering (ChebNet); Deferrard et al
  2. Graph Convolutional Neural Networks; Kipf et al.
  3. Graph Attention Networks (GAT); Velickovic et al.
  4. Inductive Representation learning in large graphs; Hamilton et al
  5. Graph Isormorphism Networks; Xu et al.
  6. Gated Graph Sequence Neural Networks; Li et al
  7. Benchmarking Graph Neural Networks; Dwivedi et al

Frameworks/ tools

  1. PyTorch Geometric (Based on pytorch)
  2. Spekral (Tensorflow/ keras)
  3. Deep Graph Library (Framework agnostic, compatible with PyTorch, Tensorflow, MxNet etc)
  4. Open Graph Benchmark: Datasets for Machine Learning on graphs