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Network Biology, 2020, 10(4): 92-107
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Article

A new method for maximizing influence on social networks based on node membership in communities

Esmaeil Bagheri
Department of Computer, Dehaghan Branch, Islamic Azad University, Isfahan, Iran

Received 13 September 2020;Accepted 3 November 2020;Published 1 December 2020
IAEES

Abstract
Influence maximization is one of the fundamental issues in social networks context. In viral marketing which is one of applications of this category, a small group of users are selected to accept a product and influence of these users on other people might result in massive acceptance of this product in social network. The influence maximization problem is choosing a set of k nodes from a social network that maximizes the influence in the network. Various studies have been conducted to find more effective k nodes for influence propagation on social networks. But the main challenges of these studies are the lack of scalability and low speed. Influential nodes must also have local influence and global influence throughout the network so that they can affect the entire network at an acceptable time. Considering the important role of influential nodes in each community for influence propagation in that community and, consequently propagating the influence throughout social network, in this paper, an algorithm is presented that maximizes the influence throughout social network through finding the nodes that have more membership strength to their community. The proposed algorithm is tested on several real and synthetic social networks. Experimental results show that the proposed method can effectively find appropriate seed nodes for influence maximization.

Keywords influence maximization;community detection;social networks.


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