# Online Influence Maximization with Semi-Bandit Feedback under Corruptions

arXiv: https://arxiv.org/abs/2409.20079 · HTML: https://mlsanity.com/paper/2409.20079/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2024.

## Built on by (most cited first, 1 of 1)

- [Anomaly Detection in Networked Bandits](https://mlsanity.com/paper/2508.20076/) (2025)

## Abstract (arXiv)

In this work, we investigate the online influence maximization in social networks. Most prior research studies on online influence maximization assume that the nodes are fully cooperative and act according to their stochastically generated influence probabilities on others. In contrast, we study the online influence maximization problem in the presence of some corrupted nodes whose damaging effects diffuse throughout the network. We propose a novel bandit algorithm, CW-IMLinUCB, which robustly learns and finds the optimal seed set in the presence of corrupted users. Theoretical analyses establish that the regret performance of our proposed algorithm is better than the state-of-the-art online influence maximization algorithms. Extensive empirical evaluations on synthetic and real-world datasets also show the superior performance of our proposed algorithm.