Over-Squashing in GNNs and Causal Inference of Rewiring Strategies
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图神经网络的过度压缩与重连策略的因果评估
- 研究问题
- 如何测量图神经网络中远距离信息的过度压缩，并区分图重连对压缩程度和分类表现的影响为何随数据集而变化？
- 方法与思路
- 作者用节点对互敏感性的衰减率衡量过度压缩，汇总为四项图级统计量，再结合图内因果设计评估不同重连策略。
- 材料陈述的贡献
- 摘要称图分类任务中重连常缓解过度压缩，但性能收益因数据和方法而异；节点分类中压缩较轻，重连可能加重问题。
- 阅读关注点
- 核对互敏感性指标与拓扑的关系、图内因果设计的识别依据，以及训练前诊断如何连接后续分类表现与重连选择。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实。摘要未给因果设计细节和结果数值，不能将所述数据集表现推广到全部图任务。
原文摘要
Graph neural networks (GNNs) have exhibited state-of-the-art performance across wide-range of domains such as recommender systems, material design, and drug repurposing. Yet message-passing GNNs suffer from over-squashing -- exponential compression of long-range information from distant nodes -- which limits expressivity. Rewiring techniques can ease this bottleneck; but their practical impacts are unclear due to the lack of a direct empirical over-squashing metric. We propose a rigorous, topology-focused method for assessing over-squashing between node pairs using the decay rate of their mutual sensitivity. We then extend these pairwise assessments to four graph-level statistics (prevalence, intensity, variability, extremity). Coupling these metrics with a within-graph causal design, we quantify how rewiring strategies affect over-squashing on diverse graph- and node-classification benchmarks. Our extensive empirical analyses show that most graph classification datasets suffer from over-squashing (but to various extents), and rewiring effectively mitigates it -- though the degree of mitigation, and its translation into performance gains, varies by dataset and method. We also found that over-squashing is less notable in node classification datasets, where rewiring often increases over-squashing, and performance variations are uncorrelated with over-squashing changes. These findings suggest that rewiring is most beneficial when over-squashing is both substantial and corrected with restraint -- while overly aggressive rewiring, or rewiring applied to minimally over-squashed graphs, is unlikely to help and may even harm performance. Our plug-and-play diagnostic tool lets practitioners decide -- before any training -- whether rewiring is likely to pay off.