Skip to Main content Skip to Navigation
Journal articles

Influence Maximization in Independent Cascade Networks Based on Activation Probability Computation

Abstract : 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 SelectTopK algorithm, the RankedReplace algorithm, and the greedy algorithm.
Complete list of metadata
Contributor : Leonardo Brenner Connect in order to contact the contributor
Submitted on : Tuesday, March 15, 2022 - 9:26:38 AM
Last modification on : Friday, April 29, 2022 - 8:38:50 AM
Long-term archiving on: : Thursday, June 16, 2022 - 6:11:48 PM


final version of access.pdf
Publisher files allowed on an open archive


Distributed under a Creative Commons Attribution - NonCommercial 4.0 International License




Wenjing yang, Leonardo Brenner, Alessandro Giua. Influence Maximization in Independent Cascade Networks Based on Activation Probability Computation. IEEE Access, IEEE, 2019, 7, pp.13745-13757. ⟨10.1109/ACCESS.2019.2894073⟩. ⟨hal-02373686⟩



Record views


Files downloads