点击次数：

论文类型： 会议论文

发表时间： 2015-09-18

收录刊物： EI、CPCI-S、Scopus

卷号： 9313

页面范围： 548-559

摘要： Influence Maximization is the problem of selecting a small set of seed users in a social network to maximize the spread of influence. Traditional solutions are mainly divided into two directions. The one is greedy-based methods and the other is heuristics-based methods. The greedy-based methods can effectively estimate influence spread using thousands of Monte-Carlo simulations. However, the computational cost of simulation is extremely expensive so that they are not scalable to large networks. The heuristics-based methods, estimating influence spread according to heuristic strategies, have low computational cost but without theoretical guarantees. In order to improve both performance and effectiveness, in this paper we propose a greedy-based algorithm, named UserGreedy. In UserGreedy, we first propose a novel concept called Activation Set, which is defined as a set of users that can be activated by a seed user with a certain probability under the most standard and popular independent cascade (IC) model. Based on the computation of such probabilities, we can directly estimate the influence spread without the expensive simulation process. We then design an influence spread function based on the the Activation Set and mathematically prove that it has the property of monotonicity and submodularity, which provides theoretical guarantee for the UserGreedy algorithm. Besides, we also propose an efficient method to obtain the Activation Set and hence implement the UserGreedy algorithm. Experiments on real-world social networks demonstrate that our algorithm is much faster than existing greedy-based algorithms and outperforms the state-of-art heuristics-based algorithms in terms of effectiveness.