Graph pooling via Dual-view Multi-level Infomax

Abstract

Graph pooling is an essential component to improve the representation ability of graph neural networks. Existing pooling methods typically select a subset of nodes to generate an induced subgraph as the representation of the entire graph. However, they ignore the potential value of augmented views and cannot exploit the multi-level dependencies between representations. To address these problems, we propose Dual-view Multi-level Infomax Pooling (DMIPool), which can obtain and maximize the multi-level mutual information across dual-view representations. First, we generate a dual-view framework for graph pooling through data augmentation to encode hierarchical information and optimize representations. Next, we design a dynamic fusion mechanism to exploit both node features and graph topology information in dual views, and achieve comprehensive node-level and graph-level representations. Finally, to evaluate and further optimize the dual-view representations, we propose multi-level infomax, which maximizes mutual information of the representations across dual views at the graph–graph level, node–node level, and graph–node level. DMIPool can learn robust representations and preserve representative nodes by leveraging multi-level dependencies between dual-view representations. Comprehensive experimental results on 9 graph classification benchmark datasets demonstrated the superior performance of DMIPool compared with 10 advanced graph pooling methods.