New centrality measure in Social Networks based on Independent Cascade (IC) model

In this paper, we consider the influence maximization problem in social networks. There are various works to maximize the influence spread. The aim is to find a k - nodes subset to maximize the influence spread in a network. We propose a new algorithm (BRST-algorithm) to determine a particular spanning tree. We also propose a new centrality measure. This heuristic is based on the diffusion probability and on the contribution of the lth neighbors to maximize the influence spread. Our heuristic uses the Independent Cascade Model (ICM). The two proposed algorithms are effective and their complexity is O(nm). The simulation of our model is done with R software and igraph package. To demonstrate the performance of our heuristic, we implement one benchmark algorithm, the diffusion degree, and we compare it with ours.