发表机构
Zhejiang University of Technology; The University of Sydney; Vecton AI（浙江工业大学; 悉尼大学; Vecton AI）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对时序图不平衡节点分类中少数类表示被同化、判别证据不足的问题，提出MDTE框架，通过分布感知选择性传播与多视图判别融合，在五个真实数据集上显著提升少数类相关指标。
AI 中文摘要
时序图上的类别不平衡节点分类具有挑战性，因为多数类主导的时序传播会逐步同化少数类表示，而传统的节点和邻域信息无法为少数类提供足够的判别证据。为解决这些问题，本文提出MDTE，一种少数类感知的扩散框架，通过条件扩散去噪重构稳定且具有判别性的时序边事件表示。具体而言，MDTE引入分布感知选择性传播，它结合基于局部异常因子（LOF）的传播过滤与集群感知的低频传播，该模块在保留有用邻域依赖的同时，缓解有害传播和多数类信息同化。它还开发了多视图判别融合，利用特征重构和拓扑预测来刻画分布学习中的类间差异，并提取互补的判别信号以指导去噪。在五个真实世界数据集上的实验表明，MDTE在面向少数类的指标上始终取得最佳性能，相较于最强基线，其少数类召回率最高提升23.53个百分点，少数类F1值最高提升8.68个百分点，AUPRC最高提升2.67个百分点。
英文摘要
Class-imbalanced node classification on temporal graphs is challenging because majority-dominated temporal propagation progressively assimilates minority representations, while conventional node and neighborhood information provides insufficient discriminative evidence for minority classes. To address these issues, we propose MDTE, a minority-aware diffusion framework that reconstructs stable and discriminative temporal edge-event representations through conditional diffusion denoising. Specifically, MDTE introduces Distribution-Aware Selective Propagation, which combines Local Outlier Factor (LOF)-based propagation filtering with cluster-aware low-frequency propagation. The module preserves informative neighborhood dependencies while mitigating harmful propagation and majority-class information assimilation. It further develops Multi-View Discriminative Fusion, which exploits feature reconstruction and topology prediction to characterize class-wise differences in distribution learning and extracts complementary discriminability signals to guide denoising. Experiments on five real-world datasets demonstrate that MDTE consistently achieves the best performance on minority-class-oriented metrics, improving minority-class recall by up to 23.53 percentage points, minority-class F1 by 8.68 percentage points, and AUPRC by 2.67 percentage points over the strongest baselines.
