International Conference on Computer and Knowledge Engineering

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An influence maximization algorithm based on community detection using topological features

Authors :

Zahra Aghaee¹ Afsaneh Fatemi²

1- Department of Software Engineering Faculty of Computer Engineering Isfahan, Iran 2- Department of Software Engineering Faculty of Computer Engineering Isfahan, Iran

Keywords :

social network, viral marketing, Influence Maximization Problem, information diffusion, community detection.

Abstract :

Due to the increasing use of social networks and the use of viral marketing in these networks, finding influential people to maximize information diffusion is considered. This problem is called Influence Maximization Problem on social networks. The main goal of the Influence Maximization Problem is to find a set of influential nodes to maximize the influence spread under a diffusion model in a social network. Researchers in this field have proposed different algorithms, but finding the influential people in the shortest possible time is still a challenge that has attracted the attention of researchers. Therefore, in this paper, the IMPT-C algorithm is presented with a focus on graph pre-processing in order to reduce the search space based on community detection. The approach of this algorithm is to take advantage of the topological properties of the graph to identify influential nodes. The experimental results show that the IMPT-C algorithm has a high influence spread with low running time compared the state-of-the-art algorithms consist least 𝟐. 𝟑𝟔% improve than the PHG algorithm in term the influence spread.

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