Subgraph-Based Networks for Expressive, Efficient, and Domain-Independent Graph Learning
Centre International de Rencontres Mathématiques via YouTube
Overview
Syllabus
Intro
Learning on graphs
Setup
Message passing Neural Networks
Color refinement (CR)
MPNNs have limited expressivity
Why expressivity matters?
Sets of subgraphs: example
Equivariant Subgraph Aggregation Networks (ESAN)
Equivariance as a design principle
Symmetry for sets of subgraphs
Detour: Deep Sets for Symmetric Elements
Equivariant layer
Subpraph selection policies
Stochastic subgraph sampling
Design choices and expressivity
Experiments
Detour: Invariant Graph Networks (IGNs)
Symmetries of node-based policies
Taught by
Centre International de Rencontres Mathématiques