Positive influence maximization in signed social networks under independent cascade model

For existing methods for positive influence maximization in signed networks, two factors prevent them from getting high-quality results. First, very few researchers consider the critical effect of negative edges on influence propagation. Second, most of those methods use Monte Carlo simulation to estimate the influence propagating of each candidate seed set. Such time-consuming simulation process hinders the application of those methods in solving real-world problems. Motivated by these limitations, this study investigates the problem of positive influence maximization in competitive signed networks. First, an opposite influence propagating model is defined by a set of propagation rules, where negative links play a more critical role than the positive ones. Second, an influence propagation function is defined to estimate the positive influence propagating of a seed set. Using such influence propagation function, the process of simulation can be avoided, and the computation time can be reduced greatly. An algorithm is presented to select the seed nodes which can obtain the largest positive influence spreading in the signed network. The algorithm employs the greedy strategy to sequentially select the seed nodes according to their spreading increments, which are estimated by the influence propagation function. Experimental results on real-world social networks show that our algorithm consistently outperforms the state-of-the-art in terms of solution quality and is several orders of magnitude faster than other methods.