提出可学习随机游走模型，提升图神经网络在分布外场景下的泛化能力。
Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective
- 用可学习的随机游走代替固定转移矩阵，捕捉不变特征
- 在多种分布偏移下实现3.87%的准确率提升
- 适合需要强泛化能力的图数据任务
图神经网络在面对分布外（OOD）数据时性能常下降。现有方法多依赖不变拓扑或图谱作为不变知识，但现实中这些并不保证存在。本文提出可学习随机游走（LRW）视角，构建LRW-OOD模型：将转移矩阵参数化为随机游走采样器与路径编码器，结合基于核密度估计的互信息损失，生成符合OOD原则的游走序列。大量实验表明，该方法在多种分布偏移下显著提升图的泛化性能，相较当前最优基线平均提升3.87%。
原文摘要 · Abstract (English)
Out-Of-Distribution (OOD) generalization has gained increasing attentions for machine learning on graphs, as graph neural networks (GNNs) often exhibit performance degradation under distribution shifts. Existing graph OOD methods tend to follow the basic ideas of invariant risk minimization and structural causal models, interpreting the invariant knowledge across datasets under various distribution shifts as graph topology or graph spectrum. However, these interpretations may be inconsistent with real-world scenarios, as neither invariant topology nor spectrum is assured. In this paper, we advocate the learnable random walk (LRW) perspective as the instantiation of invariant knowledge, and propose LRW-OOD to realize graph OOD generalization learning. Instead of employing fixed probability transition matrix (i.e., degree-normalized adjacency matrix), we parameterize the transition matrix with an LRW-sampler and a path encoder. Furthermore, we propose the kernel density estimation (KDE)-based mutual information (MI) loss to generate random walk sequences that adhere to OOD principles. Extensive experiment demonstrates that our model can effectively enhance graph OOD generalization under various types of distribution shifts and yield a significant accuracy improvement of 3.87% over state-of-the-art graph OOD generalization baselines.
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。