Fuzzy Overlapping Community Guided Hierarchical Self-Attention Pooling

Graph pooling aims at extracting vital information for graph coarsening, and thus helping graph neural networks to improve their graph representation ability. However, existing methods either compress similar nodes by a clustering algorithm or discard unimportant nodes by an importance score, resulting in the coarsened graph suffering from different issues. Therefore, aiming at the goal of breaking through their limitation, this paper develops a fuzzy overlapping community guided hierarchical self-attention pooling, namely FOCoHSAP. FOCoHSAP performs node selection within every detected community rather than the entire graph, which allows for an explicit preservation of both topology hierarchy and detailed node information. Especially, FOCoHSAP introduces fuzzy overlapping community detection algorithm to explore those fine-grained relationships between nodes and communities, allowing for a more promising learning of graph representation that can be utilized for accurate graph classification.