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Classroom Contents
Subgraph-Based Networks for Expressive, Efficient, and Domain-Independent Graph Learning
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- 1 Intro
- 2 Learning on graphs
- 3 Setup
- 4 Message passing Neural Networks
- 5 Color refinement (CR)
- 6 MPNNs have limited expressivity
- 7 Why expressivity matters?
- 8 Sets of subgraphs: example
- 9 Equivariant Subgraph Aggregation Networks (ESAN)
- 10 Equivariance as a design principle
- 11 Symmetry for sets of subgraphs
- 12 Detour: Deep Sets for Symmetric Elements
- 13 Equivariant layer
- 14 Subpraph selection policies
- 15 Stochastic subgraph sampling
- 16 Design choices and expressivity
- 17 Experiments
- 18 Detour: Invariant Graph Networks (IGNs)
- 19 Symmetries of node-based policies
