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MDTE: Minority-Aware Diffusion over Temporal Edge Events for Imbalanced Node Classification — arXiv:2608.24812

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.24812
License (as archived): CC0-1.0
Archived: 2026-10-18T06:36:26.737Z
SHA-256 of the archived text: a95d67cf9fc64163de1736597e63c6f22d49d8a4b47c1e5e44f8751e8666cdf4
Stable URL of this page: https://metraipolis.com/documento/dc_fc87f7ef646f

How to cite

MDTE: Minority-Aware Diffusion over Temporal Edge Events for Imbalanced Node Classification — arXiv:2608.24812 — arXiv (en), https://arxiv.org/abs/2608.24812 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fc87f7ef646f (Metraipolis archive dc_fc87f7ef646f, sha256 a95d67cf9fc64163de1736597e63c6f22d49d8a4b47c1e5e44f8751e8666cdf4)

BibTeX

@misc{dcfc87f7ef646f,
  title = {MDTE: Minority-Aware Diffusion over Temporal Edge Events for Imbalanced Node Classification — arXiv:2608.24812},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.24812},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fc87f7ef646f; sha256 a95d67cf9fc64163de1736597e63c6f22d49d8a4b47c1e5e44f8751e8666cdf4},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.24812. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-26 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2608.24812 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T06:28:52.283Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-08-26?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).