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.

翻译：图神经网络（GNNs）已被证明在处理和图结构数据学习方面非常有效。然而，先前的工作主要关注理解单个图输入，而许多实际应用需要对图结构数据进行成对分析（例如场景图匹配、代码搜索和药物-药物相互作用预测）。为此，近期工作已将重点转向学习图对之间的交互。尽管这些方法在性能上有所提升，但仍存在局限性：交互仅在节点级别上考虑，导致计算成本高且性能次优。为解决这一问题，我们提出了一种新颖且高效的图级别方法，通过图池化中的协同注意力来提取交互表示。我们的方法——协同注意力图池化（CAGPool），在使用真实世界数据集的分类和回归任务中，相对于现有方法展现出具有竞争力的性能，同时保持了较低的计算复杂度。