Anomaly Detection in Networked Bandits

• URL: http://arxiv.org/abs/2508.20076v1
• Date: Wed, 27 Aug 2025 17:41:40 GMT
• Title: Anomaly Detection in Networked Bandits
• Authors: Xiaotong Cheng, Setareh Maghsudi,
• Abstract summary: We introduce a novel bandit algorithm to address the problem of abnormal nodes on a social network.<n>Through network knowledge, the method characterizes the users' preferences and residuals of feature information.<n>It develops a personalized recommendation strategy for each user and simultaneously detects anomalies.<n>We rigorously prove an upper bound on the regret of the proposed algorithm and experimentally compare it with several state-of-the-art collaborative contextual bandit algorithms.
• Score: 11.710948334627306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The nodes' interconnections on a social network often reflect their dependencies and information-sharing behaviors. Nevertheless, abnormal nodes, which significantly deviate from most of the network concerning patterns or behaviors, can lead to grave consequences. Therefore, it is imperative to design efficient online learning algorithms that robustly learn users' preferences while simultaneously detecting anomalies. We introduce a novel bandit algorithm to address this problem. Through network knowledge, the method characterizes the users' preferences and residuals of feature information. By learning and analyzing these preferences and residuals, it develops a personalized recommendation strategy for each user and simultaneously detects anomalies. We rigorously prove an upper bound on the regret of the proposed algorithm and experimentally compare it with several state-of-the-art collaborative contextual bandit algorithms on both synthetic and real-world datasets.

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