Hierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learning (Extended Abstract)

Ning Liu, Songlei Jian, Dongsheng Li, Yiming Zhang, Zhiquan Lai, Hongzuo Xu

Abstract: Graph pooling technique in GNNs for learning expressive graph-level representation is critical yet still chal-lenging. Existing pooling methods either struggle to capture local substructures or fail to utilize high-order dependency, thus diminishing the expression capability. To solve this problem, we propose HAP, a hierarchical graph-level representation learning framework adaptively sensitive to graph structures. Specifically, HAP utilizes a novel cross-level attention mechanism MOA to naturally focus more on the close neighborhood while effectively capturing higher-order dependency. It also learns a global graph content GCont that extracts the graph pattern properties to stabilize the pre- and post-coarsening graph content, thus providing global guidance in graph coarsening. Experiments show that HAP significantly outperforms the state-of-the-art graph pooling methods.