PISA通过动态聚合机制提升图数据OOD泛化能力
PISA: Prioritized Invariant Subgraph Aggregation
- 引入动态MLP聚合器，优先整合关键子图表示
- 在15个数据集上最高提升5%分类准确率
- 适合需要强鲁棒性的图学习场景
近期研究将分布外（OOD）泛化的不变性原则从欧几里得数据扩展到图数据，但节点属性和拓扑结构的复杂性与多样分布偏移带来了挑战。Chen等人（2022b）提出CIGA，利用因果建模和信息论目标提取单一不变子图以捕捉因果特征。然而，单一子图关注可能遗漏多个因果模式。Liu等人（2025）提出SuGAr，通过采样器和多样性正则化学习并聚合多种不变子图，提升了鲁棒性，但仍依赖简单的均匀或贪婪聚合。为此，本文提出PISA框架，引入基于动态MLP的聚合机制，更有效地优先整合子图表示。在15个数据集（包括DrugOOD，Ji等，2023）上的实验表明，PISA相比先前方法最高可提升5%分类准确率。
原文摘要 · Abstract (English)
Recent work has extended the invariance principle for out-of-distribution (OOD) generalization from Euclidean to graph data, where challenges arise due to complex structures and diverse distribution shifts in node attributes and topology. To handle these, Chen et al. proposed CIGA (Chen et al., 2022b), which uses causal modeling and an information-theoretic objective to extract a single invariant subgraph capturing causal features. However, this single-subgraph focus can miss multiple causal patterns. Liu et al. (2025) addressed this with SuGAr, which learns and aggregates diverse invariant subgraphs via a sampler and diversity regularizer, improving robustness but still relying on simple uniform or greedy aggregation. To overcome this, the proposed PISA framework introduces a dynamic MLP-based aggregation that prioritizes and combines subgraph representations more effectively. Experiments on 15 datasets, including DrugOOD (Ji et al., 2023), show that PISA achieves up to 5% higher classification accuracy than prior methods.
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