Work

Anomaly Detection in Networks via Score-Based Generative Models

Year: 2023

Type: preprint

Abstract: 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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2306.15324

Authors: Gavrilev, Dmitrii, Burnaev, Evgeny

Institutions: Skolkovo Institute of Science and Technology, AIRI - Artificial Intelligence Research Institute

Language: English

Topic: Advanced Graph Neural Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4382491484

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