通过重连图结构缓解GNN的信息挤压与过度平滑问题
Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey
- 设计新图结构以优化信息在节点间的传播路径
- 改善远距离节点间信息传递，提升模型性能
- 适合研究GNN架构优化的学者和工程师
图神经网络在处理图结构数据时表现出强大能力，但其效果常受两个关键挑战限制：过挤（over-squashing），即远距离节点信息被过度压缩；过平滑（over-smoothing），即多次消息传递使节点表示趋于相同。这两种现象均源于消息传递机制与原始拓扑之间的相互作用，最终损害信息流动并限制模型性能。本文综述了图重连技术——一类通过修改图拓扑以增强图神经网络中信息传播的方法。系统梳理了前沿重连方法的理论基础、实现方式及性能权衡，涵盖从理论分析到实际应用的完整脉络。
原文摘要 · Abstract (English)
Graph Neural Networks are powerful models for learning from graph-structured data, yet their effectiveness is often limited by two critical challenges: over-squashing, where information from distant nodes is excessively compressed, and over-smoothing, where repeated propagation makes node representations indistinguishable. Both phenomena stem from the interaction between message passing and the input topology, ultimately degrading information flow and limiting the performance of GNNs. In this survey, we examine graph rewiring techniques, a class of methods designed to modify the graph topology to enhance information propagation in GNNs. We provide a comprehensive review of state-of-the-art rewiring approaches, delving into their theoretical underpinnings, practical implementations, and performance trade-offs.
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