Influence Blocking Maximization in Social Networks under the Competitive Linear Threshold Model Technical Report

TL;DR

This paper introduces the influence blocking maximization problem in social networks under the competitive linear threshold model, proposing an efficient algorithm that balances accuracy and computational speed.

Contribution

It proves the submodularity of the influence blocking objective and develops CLDAG, an efficient algorithm for influence blocking in competitive social influence scenarios.

Findings

CLDAG achieves similar accuracy to the greedy algorithm.

CLDAG is two orders of magnitude faster than the greedy approach.

The influence blocking problem is submodular under the CLT model.

Abstract

In many real-world situations, different and often opposite opinions, innovations, or products are competing with one another for their social influence in a networked society. In this paper, we study competitive influence propagation in social networks under the competitive linear threshold (CLT) model, an extension to the classic linear threshold model. Under the CLT model, we focus on the problem that one entity tries to block the influence propagation of its competing entity as much as possible by strategically selecting a number of seed nodes that could initiate its own influence propagation. We call this problem the influence blocking maximization (IBM) problem. We prove that the objective function of IBM in the CLT model is submodular, and thus a greedy algorithm could achieve 1-1/e approximation ratio. However, the greedy algorithm requires Monte-Carlo simulations of competitive influence propagation, which makes the algorithm not efficient. We design an efficient algorithm CLDAG, which utilizes the properties of the CLT model, to address this issue. We conduct extensive simulations of CLDAG, the greedy algorithm, and other baseline algorithms on real-world and synthetic datasets. Our results show that CLDAG is able to provide best accuracy in par with the greedy algorithm and often better than other algorithms, while it is two orders of magnitude faster than the greedy algorithm.