RESUMO
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in graph-related tasks. For graph classification task, an elaborated pooling operator is vital for learning graph-level representations. Most pooling operators derived from existing GNNs generate a coarsen graph through ordering the nodes and selecting some top-ranked ones. However, these methods fail to explore the fundamental elements other than nodes in graphs, which may not efficiently utilize the structure information. Besides, all edges attached to the low-ranked nodes are discarded, which destroys graphs' connectivity and loses information. Moreover, the selected nodes tend to concentrate on some substructures while overlooking information in others. To address these challenges, we propose a novel pooling operator called Accurate Structure-Aware Graph Pooling (ASPool), which can be integrated into various GNNs to learn graph-level representation. Specifically, ASPool adaptively retains a subset of edges to calibrate the graph structure and learns the abstracted representations, wherein all the edges are viewed as non-peers instead of simply connecting nodes. To preserve the graph's connectivity, we further introduce the selection strategy considering both top-ranked nodes and dropped edges. Additionally, ASPool performs a two-stage calculation process to promise that the sampled nodes are distributed throughout the graph. Experiment results on 9 widely used benchmarks show that ASPool achieves superior performance over the state-of-the-art graph representation learning methods.
