提升图神经网络对节点簇间连接关系的捕捉能力
ICEPool: Enhancing Graph Pooling Networks with Inter-cluster Connectivity
- 通过显式建模簇间连接增强图池化
- 在多个图分类任务上显著提升模型性能
- 适合希望改进现有图网络结构的开发者
层次化池化模型在图结构数据分类中表现优异。尽管已有诸多创新方法用于设计聚类分配与粗化策略，但簇间的关联关系常被忽略。本文提出一种新型层次化池化框架——互簇连通性增强池化（ICEPool），旨在增强模型对簇间连通性的理解及保留原始图结构完整性的能力。ICEPool可兼容多种基于池化的图神经网络模型。将其作为增强模块部署于现有模型，能有效融合原模型优势与ICEPool对簇间连通性的强调，获得更全面、鲁棒的图级表征。我们还进行了理论分析，验证了ICEPool在图重构上的能力，证明其能学习到传统模型忽略的簇间关系。实验结果表明，ICEPool具备广泛兼容性，并能显著提升现有图神经网络架构的性能。
原文摘要 · Abstract (English)
Hierarchical Pooling Models have demonstrated strong performance in classifying graph-structured data. While numerous innovative methods have been proposed to design cluster assignments and coarsening strategies, the relationships between clusters are often overlooked. In this paper, we introduce Inter-cluster Connectivity Enhancement Pooling (ICEPool), a novel hierarchical pooling framework designed to enhance model's understanding of inter-cluster connectivity and ability of preserving the structural integrity in the original graph. ICEPool is compatible with a wide range of pooling-based GNN models. The deployment of ICEPool as an enhancement to existing models effectively combines the strengths of the original model with ICEPool's capability to emphasize the integration of inter-cluster connectivity, resulting in a more comprehensive and robust graph-level representation. Moreover, we make theoretical analysis to ICEPool's ability of graph reconstruction to demonstrate its effectiveness in learning inter-cluster relationship that is overlooked by conventional models. Finally, the experimental results show the compatibility of ICEPool with wide varieties of models and its potential to boost the performance of existing graph neural network architectures.
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