MDTE框架在处理时间图上的节点分类问题时，特别针对少数类节点，通过创新的扩散降噪技术，显著提升了分类效果，是解决节点分类不平衡问题的有力工具。
MDTE，一种针对时间图上节点分类的少数类感知扩散框架，通过条件扩散降噪重建稳定且具有判别性的时间边事件表示。实验表明，MDTE在少数类相关指标上表现优异，比最强基线提高了23.53个百分点的少数类召回率、8.68个百分点的少数类F1值和2.67个百分点的AUPRC值。
MDTE: Minority-Aware Diffusion over Temporal Edge Events for Imbalanced Node Classification
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.