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Graph Neural Networks (GNNs) have proven to be effective in processing and
learning from graph-structured data. However, previous works mainly focused on
understanding single graph inputs while many real-world applications require
pair-wise analysis for graph-structured data (e.g., scene graph matching, code
searching, and drug-drug interaction prediction). To this end, recent works
have shifted their focus to learning the interaction between pairs of graphs.
Despite their improved performance, these works were still limited in that the
interactions were considered at the node-level, resulting in high computational
costs and suboptimal performance. To address this issue, we propose a novel and
efficient graph-level approach for extracting interaction representations using
co-attention in graph pooling. Our method, Co-Attention Graph Pooling
(CAGPool), exhibits competitive performance relative to existing methods in
both classification and regression tasks using real-world datasets, while
maintaining lower computational complexity.
