Robust Influence Blocking Maximization in Social Networks

Influence blocking maximization is currently a problem of great interest in the research area of social networks. Existing influence blocking methods assume negative influence sources are definitely known or non-adversarial. However, in real applications, it is hard to obtain the accurate information of influence sources. In addition, adversarial spreader may strategically select the seeds to maximize negative spread. In this paper, we aim to tackle the problems of influence blocking maximization with uncertain and strategic negative influence sources respectively. First, in order to increase the efficiency of the algorithms for mining blocking seeds, we discuss approximate estimation of the influence spread of negative seeds under the competitive linear threshold model. Based on the estimation, we propose a blocking seeds mining algorithm under the situation of finite uncertain and negative influence sources to maximize the expected influence blocking utility. In adversarial influence spread situations, one entity strategically attempts to maximize negative influence spread while the other one tries to block the negative propagation of its competing entity as much as possible by selecting a number of seed nodes that could initiate its own influence propagation. We model the interaction as a game and propose a polynomial time approximate algorithm for solving random blocking strategy based on min-max principle to decrease the worst negative influence spread. Finally, we make experiments on real data sets of social networks to verify the feasibility and scalability of the proposed algorithms.