MDTE: Minority-Aware Diffusion over Temporal Edge Events for Imbalanced Node Classification

论文概要

研究领域: ML
作者: Zhou Zelong, Zhang Tianming, Yang Zhengyi, Tang Yifu, Hou Chenyu等
发布时间: 2026-08-25
arXiv: 2608.24812

中文摘要

时序图上的类别不平衡节点分类具有挑战性，因为多数主导的时间传播逐步同化少数表征，而常规节点和邻域信息为少数类别提供不足的判别证据。为解决这些问题，我们提出MDTE，一种少数感知扩散框架，通过条件扩散去噪重建稳定且判别性的时间边事件表征。具体而言，MDTE引入分布感知选择性传播，结合基于局部异常因子（LOF）的传播过滤和聚类感知低频传播。该模块保留信息性邻域依赖，同时缓解有害传播和多数类信息同化。它进一步开发多视图判别融合，利用特征重建和拓扑预测来表征分布学习中的类别差异，并提取互补判别信号指导去噪。

原文摘要

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 infor...

自动采集于 2026-08-27

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