An Approach to Maximize the Influence Spread in the Social Networks

In this paper, we treat the influence maximization problem in social networks. In this work, we attach importance to the initial diffusers called the seeds. The main goal is to find in a given social network an effective subset of kusers which begin the influence maximization. Our approach is to prevent information feedback to seeds while their selection. This prevention is to extract a specific spanning graph before the determination of seeds. At first, we propose two algorithms called SCG v1-algorithm and SCG v2-algorithm. The first algorithm randomly builds the children of the nodes while the second uses the neighborhood for the construction of the children nodes. These two versions take as input data a connected graph. So, we propose a generalization of both versions called SG-algorithm which takes as input data an arbitrary graph. These algorithms are effective and have each one a polynomial complexity. To show the pertinence of our approach, three seeds sets are determined and the one given by our approach gives better results. The performances of this approach are very perceptible through the simulation carried out by the R software and the igraph package.

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