Title: Identifying influential nodes in large scale social networks using global and local structural information

Authors: Noosheen Shareefi; Mehdi Bateni

Addresses: Department of Computer Science, Sheikhbahaee University, Isfahan, Iran ' Department of Computer Science, Khansar Campus, University of Isfahan, Isfahan, Iran

Abstract: Due to the importance of identifying influential nodes in different applications, many methods have been proposed for it. Some of them are not accurate enough or have high temporal complexity. In this paper, a method named new GLS (NGLS) is developed based on the global and local search (GLS) algorithm. GLS, despite its high accuracy compared to other methods is not fast and efficient enough. NGLS is developed to improve the efficiency and scalability of GLS. To reach this goal, the number of common neighbours of each node is counted only up to a radius of two. The execution time of NGLS on average has been reduced by 85% in real-world networks and 97% on simulated networks, while the accuracy of NGLS is the same as GLS accuracy. Therefore, NGLS is applicable for larger real-world networks.

Keywords: influential nodes; global structure; local structure; large networks; centrality measure; neighbour contribution; complex network; propagation; propagation models; social influence analysis; social network analysis.

DOI: 10.1504/IJBIDM.2023.132595

International Journal of Business Intelligence and Data Mining, 2023 Vol.23 No.2, pp.150 - 165

Received: 30 Dec 2021
Accepted: 05 Apr 2022

Published online: 30 Jul 2023 *

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