Work

Trivial Graph Features and Classical Learning are Enough to Detect Random Anomalies

Year: 2025

Type: conference-paper

Abstract: Detecting anomalies in link streams that represent various kinds of interactions is an important research topic with crucial applications. Because of the lack of ground truth data, proposed methods are mostly evaluated through their ability to detect randomly injected links. In contrast with most proposed methods, that rely on complex approaches raising computational and/or interpretability issues, we show here that trivial graph features and classical learning techniques are sufficient to detect such anomalies extremely well. This basic approach has very low computational costs and it leads to easily interpretable results. It also has many other desirable properties that we study through an extensive set of experiments. We conclude that detection methods should now target more complex kinds of anomalies.

Source: Proceedings

DOI: 10.1109/icdm65498.2025.00142

Authors: Matthieu Latapy, Stephany Rajeh

Institutions: Centre National de la Recherche Scientifique, Sorbonne Université, LIP6, Université Paris-Panthéon-Assas

Language: English

Topic: Anomaly Detection Techniques and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Agence Nationale de la Recherche, Centre National de la Recherche Scientifique

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