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

arXiv · AI, language, vision and robotics · article · Aug 25, 2026 · UTC

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

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