ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations

Understand

Graph Neural Networks (GNN) have been shown to work effectively for modeling graph structured data to solve tasks such as node classification, link prediction and graph classification.

• There has been some recent progress in defining the notion of pooling in graphs whereby the model tries to generate a graph level representation by downsampling and summarizing the information present in the nodes.
• Existing pooling methods either fail to effectively capture the graph substructure or do not easily scale to large graphs.
• In this work, we propose ASAP (Adaptive Structure Aware Pooling), a sparse and differentiable pooling method that addresses the limitations of previous graph pooling architectures.