DiffPool

2000년 도입 · 논문 7편에서 사용

DiffPool is a differentiable graph pooling module that can generate hierarchical representations of graphs and can be combined with various graph neural network architectures in an end-to-end fashion. DiffPool learns a differentiable soft cluster assignment for nodes at each layer of a deep GNN, mapping nodes to a set of clusters, which then form the coarsened input for the next GNN layer. Description and image from: Hierarchical Graph Representation Learning with Differentiable Pooling

출처: Hierarchical Graph Representation Learning with Differentiable Pooling

소개 논문: Hierarchical Graph Representation Learning with Differentiable Pooling