可微Koopman算子用于动态图上的对比学习

中文摘要 AI 辅助

现实世界的交互网络本质上是动态的：随着节点行为随时间变化，边会形成和消失。大多数基于快照的对比方法将时间依赖性隐式编码在编码器权重中，而没有显式建模节点表示如何演化，这使得它们在分布偏移下表现脆弱。我们提出KAIROS（开放动态系统的Koopman对齐不变表示），一个自监督框架，在动态图对比学习循环中嵌入可微Koopman算子，以在学习的嵌入空间中线性化时间演化。双视图编码器将原始节点特征与图扩散结构视图配对，并通过跨时间窗口的多粒度对比目标进行优化。对于异常检测，KAIROS使用Koopman预测残差以及时间不一致性和局部邻域偏差，将不规则行为与可预测的图演化区分开来。在九个动态图基准上评估，KAIROS在所有九个数据集上实现了最先进的异常检测结果，相比先前工作最高提升23.15 ROC-AUC点，同时在无监督节点分类方面保持竞争力。这些结果表明，显式动力学建模为时间图表示学习提供了可扩展且有效的归纳偏置。

英文摘要

Real-world interaction networks are inherently dynamic: edges form and dissolve as node behavior shifts over time. Most snapshot-based contrastive methods encode temporal dependencies implicitly in encoder weights, without an explicit model of how node representations evolve, making them brittle under distribution shifts. We propose KAIROS (Koopman-Aligned Invariant Representations for Open Dynamic Systems), a self-supervised framework that embeds a differentiable Koopman operator within a dynamic graph contrastive learning loop to linearize temporal evolution in the learned embedding space. A dual-view encoder pairs raw node features with a graph-diffused structural view and is optimized with multi-granularity contrastive objectives across temporal windows. For anomaly detection, KAIROS uses the Koopman prediction residual together with temporal inconsistency and local neighborhood deviation to separate irregular behavior from predictable graph evolution. Evaluated on nine dynamic graph benchmarks, KAIROS achieves state-of-the-art anomaly detection results on all nine datasets, with gains of up to 23.15 ROC-AUC points over prior work, while remaining competitive for unsupervised node classification. These results show that explicit dynamics modeling provides a scalable and effective inductive bias for temporal graph representation learning.

发表机构

• University of Southern California（南加州大学）
• Islamic University of Technology（伊斯兰理工大学）
• Qatar Computing Research Institute (QCRI)（卡塔尔计算研究所）

机构由 AI 辅助整理，请以论文原文为准。