Online Influence Maximization with Semi-Bandit Feedback under Corruptions

TL;DR

This paper introduces a robust bandit algorithm for online influence maximization in social networks that effectively handles corrupted nodes, outperforming existing methods in theory and practice.

Contribution

The paper proposes CW-IMLinUCB, a novel algorithm that robustly learns influence maximization strategies despite network corruptions, with proven regret bounds and superior empirical results.

Findings

The algorithm achieves lower regret than state-of-the-art methods.

It performs well on both synthetic and real-world datasets.

Theoretical analysis confirms improved regret bounds.

Abstract

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.