Scalable Adversarial Attack Algorithms on Influence Maximization

TL;DR

This paper develops scalable algorithms for adversarially attacking influence maximization in social networks, aiming to reduce influence spread by node and edge removal under the linear threshold model.

Contribution

It introduces efficient reverse influence sampling-based algorithms with approximation guarantees for influence reduction under adversarial attacks.

Findings

Algorithms guarantee a 1/2 - ε approximation.

Proposed methods are computationally efficient.

Applicable to blocking virus, rumor, or competitor influence.

Abstract

In this paper, we study the adversarial attacks on influence maximization under dynamic influence propagation models in social networks. In particular, given a known seed set S, the problem is to minimize the influence spread from S by deleting a limited number of nodes and edges. This problem reflects many application scenarios, such as blocking virus (e.g. COVID-19) propagation in social networks by quarantine and vaccination, blocking rumor spread by freezing fake accounts, or attacking competitor's influence by incentivizing some users to ignore the information from the competitor. In this paper, under the linear threshold model, we adapt the reverse influence sampling approach and provide efficient algorithms of sampling valid reverse reachable paths to solve the problem. We present three different design choices on reverse sampling, which all guarantee $1/2 - \varepsilon$ approximation (for any small $\varepsilon >0$) and an efficient running time.