Communication Dans Un Congrès Année : 2025

A Modular Triple Exchange Co-learning Framework for Anomaly Detection in Scarcely Labeled Graph Data

Résumé

Graph anomaly detection (GAD) is a fundamental task in numerous real-world applications, including fraud detection, network security, and financial risk assessment. However, existing approaches struggle with label scarcity, limiting the effectiveness of purely supervised and semi-supervised models. Co-training frameworks have demonstrated improvements through collaborative learning, but they remain constrained by their rigid structure and reliance on graph-specific architectures. In this paper, we introduce TExGAD, a novel collaborative learning framework that extends co-training by introducing a modular triple-exchange mechanism that leverages three detectors: (1) a flexible Semi-supervised Detector, allowing for the integration of both shallow and non-graph-specific models, (2) an Unsupervised Detector that identifies structural deviations in the graph without relying on labeled data, making it particularly effective at detecting normal nodes and scattered anomalies, and (3) a Deep Detector that captures richer anomaly representations by leveraging high-confidence predictions from the other detectors. Furthermore, TExGAD integrates a set of specialized loss functions, ensuring a balance between diversity, alignment, and independent learning and an adaptive scoring function that further refines anomaly detection by dynamically weighting detector contributions based on their confidence. Our experiments demonstrate the effectiveness of TExGAD in low-label environments, highlighting its adaptability and robustness.

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Dates et versions

hal-05139666 , version 1 (02-07-2025)

Identifiants

Citer

Cyril Perosino, Hamida Seba, Walid Megherbi, Mohammed Haddad. A Modular Triple Exchange Co-learning Framework for Anomaly Detection in Scarcely Labeled Graph Data. Graph-Based Representations in Pattern Recognition Conference paper, Jun 2025, Caen, France. pp.3-12, ⟨10.1007/978-3-031-94139-9_1⟩. ⟨hal-05139666⟩
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