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In many real-world situations, different and often opposite opinions,innovations, or products are competing with one another for their socialinfluence in a networked society. In this paper, we study competitive influencepropagation 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 influencepropagation of its competing entity as much as possible by strategicallyselecting a number of seed nodes that could initiate its own influencepropagation. We call this problem the influence blocking maximization (IBM)problem. We prove that the objective function of IBM in the CLT model issubmodular, and thus a greedy algorithm could achieve 1-1/e approximationratio. However, the greedy algorithm requires Monte-Carlo simulations ofcompetitive influence propagation, which makes the algorithm not efficient. Wedesign an efficient algorithm CLDAG, which utilizes the properties of the CLTmodel, to address this issue. We conduct extensive simulations of CLDAG, thegreedy algorithm, and other baseline algorithms on real-world and syntheticdatasets. Our results show that CLDAG is able to provide best accuracy in parwith the greedy algorithm and often better than other algorithms, while it istwo orders of magnitude faster than the greedy algorithm.

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