Co-attention Graph Pooling for Efficient Pairwise Graph Interaction Learning

TL;DR

This paper introduces CAGPool, a novel graph pooling method that efficiently captures pairwise graph interactions at the graph level, reducing computational costs while maintaining competitive performance.

Contribution

It proposes a new co-attention based graph pooling technique for pairwise graph interaction learning, addressing node-level limitations and computational inefficiency.

Findings

Competitive performance on classification and regression tasks

Lower computational complexity compared to existing methods

Effective at capturing graph-level interactions

Abstract

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.