通过重连图结构缓解图神经网络的信息压缩与同质化问题
Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey
- 通过调整图拓扑结构优化信息传递路径
- 有效缓解远距离节点信息被过度压缩的问题
- 适合研究图神经网络优化的学者与工程实践者
图神经网络在处理图结构数据时表现出强大能力，但其性能常受两个关键挑战限制：过压缩（over-squashing），即远距离节点信息被过度压缩；过平滑（over-smoothing），即多次消息传递导致节点表示趋于相同。这两种现象均源于消息传递机制与输入拓扑之间的相互作用，最终损害信息流动并限制模型表现。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、实现方式及其性能权衡，为理解与改进图神经网络提供了全面视角。
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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