Influence Maximization in Independent Cascade Networks Based on Activation Probability Computation

Based on the concepts of “word-of-mouth” effect and viral marketing, the diffusion of an innovation may be triggered starting from a set of initial users. Estimating the influence spread is a preliminary step to determine a suitable or even optimal set of initial users to reach a given goal. In this paper, we focus on a stochastic model called the independent cascade model and compare a few approaches to compute activation probabilities of nodes in a social network, i.e., the probability that a user adopts the innovation. First, we propose the path method that computes the exact value of the activation probabilities but has high complexity. Second, an approximated method, called SSS-Noself, is obtained by the modification of the existing SteadyStateSpread algorithm, based on fixed-point computation, to achieve better accuracy. Finally, an efficient approach, also based on fixed-point computation, is proposed to compute the probability that a node is activated through a path of minimal length from the seed set. This algorithm, called SSS-Bounded-Path algorithm, can provide a lower bound for the computation of activation probabilities. Furthermore, these proposed approaches are applied to the influence maximization problem combined with the SelectTop$K$ algorithm, the RankedReplace algorithm, and the greedy algorithm.