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RagGAD: Rationale-Aware Conditional Gaussian Mixture Normalizing Flow for Unsupervised Graph Anomaly Detection — arXiv:2608.16018

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.16018
License (as archived): CC0-1.0
Archived: 2026-10-18T18:06:53.554Z
SHA-256 of the archived text: 2040c38681f1b90965f93e236e5e556ac6a39899fadcf3b1b761e8c321c52609
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How to cite

RagGAD: Rationale-Aware Conditional Gaussian Mixture Normalizing Flow for Unsupervised Graph Anomaly Detection — arXiv:2608.16018 — arXiv (en), https://arxiv.org/abs/2608.16018 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_56bb493cdeee (Metraipolis archive dc_56bb493cdeee, sha256 2040c38681f1b90965f93e236e5e556ac6a39899fadcf3b1b761e8c321c52609)

BibTeX

@misc{dc56bb493cdeee,
  title = {RagGAD: Rationale-Aware Conditional Gaussian Mixture Normalizing Flow for Unsupervised Graph Anomaly Detection — arXiv:2608.16018},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.16018},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_56bb493cdeee; sha256 2040c38681f1b90965f93e236e5e556ac6a39899fadcf3b1b761e8c321c52609},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.16018. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Graph anomaly detection aims to identify nodes that deviate from normal behavioral patterns within graphs. However, existing methods largely rely on the homophily assumption, which makes it difficult to distinguish spurious affinities and to capture the diverse behaviors of normal nodes,limiting their robustness in complex real-world scenarios. To address this problem, we propose RagGAD, an unsupervised graph anomaly detection framework based on rationale-aware conditional Gaussian mixture normalizing flow. RagGAD introduces an adaptive rationale disentangler to disentangle stable rationales from spurious correlations within node interrelationships, and further decomposes stable rationales into robust and fragile components. The learned rationales capture underlying interaction patterns that characterize normal behaviors under varying conditions, while anomalies emerge as deviations associated with unstable or spurious correlations. To model the intricate distributions of normal and abnormal nodes, RagGAD integrates rationale-non-rationale Gaussian mixture modeling with a robust-fragile rationale mixture learning strategy. By mitigating spurious homophilic correlations and embracing the heterogeneity of normal patterns, RagGAD identifies anomalies as low-density regions within a structure-aware distribution space. Extensive experiments on multiple benchmark datasets demonstrate that RagGAD outperforms state-of-the-art methods.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-18 in cs.AI).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.16018 — 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 2026-10-18T16:29:14.069Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-18?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).