ADP: Graph Adaptive Pooling Based on Edge Understanding With Graph Pooling Information Bottleneck
Zhe Cao, Lixin Xu, Ruiheng Zhang, Jin Zhang, Hongbin Pei, Daming Zhou, Jiayan Qiu
Abstract
Graph pooling plays a crucial role in achieving effective local information aggregation. When dealing with graph, data in non-Euclidean space, a major challenge lies in the uncertainty of the number of nodes. Graph pooling with self-adaptivity can capture the optimal information region to aggregate shared information within this region. Existing methods rely on hard thresholds or parameter settings, making it hard to achieve real adaptiveness and often compromising structural integrity. Inspired by the special relationship between edges and nodes, we propose a real ADaptive Pooling (ADP) method, requiring no hyper-parameter tuning. Specifically, we obtain the graph’s main structure by filtering the most significant edges of each node. Subsequently, we utilize the naturally formed connected regions of n-edge graphs to define information regions. Finally, we integrate the features of nodes within these regions and establish new connections between the nodes. We hold these regions’ structural integrity and stability with triplet loss and information bottleneck compression loss. Our method achieves adaptive pooling while preserving graph structural features, and it has demonstrated state-of-the-art performance on 12 graph datasets in 4 types.