Co-attention Graph Pooling for Efficient Pairwise Graph Interaction Learning
This addresses the need for efficient pairwise graph analysis in applications like drug interaction prediction, though it is an incremental improvement over existing methods.
The paper tackled the problem of inefficient pairwise graph interaction learning in Graph Neural Networks (GNNs) by proposing a graph-level co-attention pooling method, achieving competitive performance with lower computational complexity on real-world datasets.
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.