用奇异值正则化让图神经网络更稳定、抗干扰。
A General Recipe for Contractive Graph Neural Networks -- Technical Report
- 通过奇异值约束让任意图神经网络具备收缩性。
- 实验证明该方法显著降低模型的Lipschitz常数。
- 适合关注模型鲁棒性与泛化能力的研究者。
图神经网络（GNN）在社交网络分析、推荐系统和生物信息学等领域广泛应用，但常面临稳定性差、泛化能力弱及对噪声和对抗攻击敏感的问题。本文基于近期收缩型GNN架构进展，提出一种通用方法：通过奇异值分解（SVD）正则化，在任意GNN中诱导收缩行为。通过推导更新步骤的收缩性充分条件，并对网络参数施加约束，证明了该方法可有效控制GNN的Lipschitz常数。实验结果表明，该正则化策略显著提升了GNN的稳定性与泛化性能，为构建更鲁棒的图学习算法提供了新路径。
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs) have gained significant popularity for learning representations of graph-structured data due to their expressive power and scalability. However, despite their success in domains such as social network analysis, recommendation systems, and bioinformatics, GNNs often face challenges related to stability, generalization, and robustness to noise and adversarial attacks. Regularization techniques have shown promise in addressing these challenges by controlling model complexity and improving robustness. Building on recent advancements in contractive GNN architectures, this paper presents a novel method for inducing contractive behavior in any GNN through SVD regularization. By deriving a sufficient condition for contractiveness in the update step and applying constraints on network parameters, we demonstrate the impact of SVD regularization on the Lipschitz constant of GNNs. Our findings highlight the role of SVD regularization in enhancing the stability and generalization of GNNs, contributing to the development of more robust graph-based learning algorithms dynamics.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。