  Graph pooling that summaries the information in a large graph into a compact
form is essential in hierarchical graph representation learning. Existing graph
pooling methods either suffer from high computational complexity or cannot
capture the global dependencies between graphs before and after pooling. To
address the problems of existing graph pooling methods, we propose Coarsened
Graph Infomax Pooling (CGIPool) that maximizes the mutual information between
the input and the coarsened graph of each pooling layer to preserve graph-level
dependencies. To achieve mutual information neural maximization, we apply
contrastive learning and propose a self-attention-based algorithm for learning
positive and negative samples. Extensive experimental results on seven datasets
illustrate the superiority of CGIPool comparing to the state-of-the-art
methods.
