让图神经网络不依赖节点编号，提升模型泛化能力
Towards Invariance to Node Identifiers in Graph Neural Networks
- 引入正则化方法强制网络对节点标识不变
- 实验表明新方法显著提升模型在真实与合成任务的泛化性能
- 适合关注图神经网络可解释性与鲁棒性的研究者
消息传递型图神经网络（GNN）因结构限制而表达能力有限。一种常见突破方式是引入唯一节点标识符（ID）以打破对称性。本文指出，当前基于ID的方法在实践中仍依赖具体编号，无法实现期望的节点标识不变性。为此，我们提出一种新颖的正则化方法，有效强制网络具备节点标识不变性。在真实世界与合成数据上的大量实验表明，该方法显著提升了模型对节点编号的不变性，并改善了泛化性能。
原文摘要 · Abstract (English)
Message-Passing Graph Neural Networks (GNNs) are known to have limited expressive power, due to their message passing structure. One mechanism for circumventing this limitation is to add unique node identifiers (IDs), which break the symmetries that underlie the expressivity limitation. In this work, we highlight a key limitation of the ID framework, and propose an approach for addressing it. We begin by observing that the final output of the GNN should clearly not depend on the specific IDs used. We then show that in practice this does not hold, and thus the learned network does not possess this desired structural property. Such invariance to node IDs may be enforced in several ways, and we discuss their theoretical properties. We then propose a novel regularization method that effectively enforces ID invariance to the network. Extensive evaluations on both real-world and synthetic tasks demonstrate that our approach significantly improves ID invariance and, in turn, often boosts generalization performance.
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