mlsanityData to 7 Oct 2026

Anomaly Detection in Networks via Score-Based Generative Models

arXiv 2306.15324 ↗First posted 27 Jun 2023cs.LGcited by 2

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Abstract (arXiv)

Node outlier detection in attributed graphs is a challenging problem for which there is no method that would work well across different datasets. Motivated by the state-of-the-art results of score-based models in graph generative modeling, we propose to incorporate them into the aforementioned problem. Our method achieves competitive results on small-scale graphs. We provide an empirical analysis of the Dirichlet energy, and show that generative models might struggle to accurately reconstruct it.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 35 of its 67 references that are in the map. First posted on arXiv 27 Jun 2023.

Builds on

35 of 67 references.

References · 35

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23 more
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Cited by year

Citing papers per year: 2023: 1; 2024: 1.

2 papers.

  1. 2023Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approachcited by 17
  2. 2024Large Generative Graph Modelscited by 2

Similar, not cited

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  8. 2022NVDiff: Graph Generation through the Diffusion of Node Vectors