How-Powerful-of-GNN

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HOW POWERFUL ARE GRAPH NEURAL NETWORKS?

This paper:

  1. characterize how expressive different GNN variants are in learning to represent and distinguish between different graph structures
  2. show that GNNs are at most as powerful as the WL test in distinguishing graph structures.
  3. identify graph structures that cannot be distinguished by popular GNN variants, such as GCN (Kipf & Welling, 2017) and GraphSAGE (Hamilton et al., 2017a)
  4. develop a simple neural architecture, Graph Isomorphism Network (GIN)