# Anomaly Detection in Networked Bandits

arXiv: https://arxiv.org/abs/2508.20076 · HTML: https://mlsanity.com/paper/2508.20076/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 45 references that are in the map.
- First posted on arXiv 27 Aug 2025.

## Builds on (most cited first, 14 of 14)

- [Online Clustering of Bandits](https://mlsanity.com/paper/1401.8257/) (2014)
- [A Gang of Bandits](https://mlsanity.com/paper/1306.0811/) (2013)
- [Sparsity-Agnostic Lasso Bandit](https://mlsanity.com/paper/2007.08477/) (2020)
- [Improved Algorithm on Online Clustering of Bandits](https://mlsanity.com/paper/1902.09162/) (2019)
- [Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits](https://mlsanity.com/paper/2110.01463/) (2021)
- [Dynamic Batch Learning in High-Dimensional Sparse Linear Contextual Bandits](https://mlsanity.com/paper/2008.11918/) (2020)
- [Multi-task Representation Learning with Stochastic Linear Bandits](https://mlsanity.com/paper/2202.10066/) (2022)
- [Fast Distributed Bandits for Online Recommendation Systems](https://mlsanity.com/paper/2007.08061/) (2020)
- [Laplacian-regularized graph bandits: Algorithms and theoretical analysis](https://mlsanity.com/paper/1907.05632/) (2019)
- [Thresholded Lasso Bandit](https://mlsanity.com/paper/2010.11994/) (2020)
- [Generic Outlier Detection in Multi-Armed Bandit](https://mlsanity.com/paper/2007.07293/) (2020)
- [Online Nonnegative Matrix Factorization with Outliers](https://mlsanity.com/paper/1604.02634/) (2016)
- [Robust Outlier Arm Identification](https://mlsanity.com/paper/2009.09988/) (2020)
- [Online Influence Maximization with Semi-Bandit Feedback under Corruptions](https://mlsanity.com/paper/2409.20079/) (2024)

## Abstract (arXiv)

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.