提出新方法精准预测图神经网络中删边增边的影响。
Influence Functions for Edge Edits in Non-Convex Graph Neural Networks
- 用近端Bregman响应函数替代凸性假设，适配非凸GNN架构。
- 同时支持删边与增边的准确影响预测，提升模型可解释性。
- 适用于图重连、对抗攻击等场景，效果优于现有方法。
理解单个边对图神经网络（GNN）行为的影响对于提升其可解释性与鲁棒性至关重要。图影响函数已成为无需重新训练即可高效估计边删除影响的有力工具。然而，现有方法依赖严格的凸性假设，仅考虑边删除而忽略边插入，并无法捕捉修改带来的消息传播变化。本文提出一种专为GNN设计的近端Bregman响应函数，放宽了凸性要求，实现对标准神经网络架构的精准影响预测。此外，该方法显式建模消息传播效应，以合理方式扩展至边删除与插入。在真实数据集上的实验表明，该方法能准确预测不同GNN特性的影响。进一步验证了其在图重连与对抗攻击等应用中的多功能性。
原文摘要 · Abstract (English)
Understanding how individual edges influence the behavior of graph neural networks (GNNs) is essential for improving their interpretability and robustness. Graph influence functions have emerged as promising tools to efficiently estimate the effects of edge deletions without retraining. However, existing influence prediction methods rely on strict convexity assumptions, exclusively consider the influence of edge deletions while disregarding edge insertions, and fail to capture changes in message propagation caused by these modifications. In this work, we propose a proximal Bregman response function specifically tailored for GNNs, relaxing the convexity requirement and enabling accurate influence prediction for standard neural network architectures. Furthermore, our method explicitly accounts for message propagation effects and extends influence prediction to both edge deletions and insertions in a principled way. Experiments with real-world datasets demonstrate accurate influence predictions for different characteristics of GNNs. We further demonstrate that the influence function is versatile in applications such as graph rewiring and adversarial attacks.
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