Vital nodes extracting method based on user's behavior in 5G mobile social networks. (1st May 2019)

• Record Type: Journal Article
• Authors: Tulu, Muluneh Mekonnen; Hou, Ronghui; Younas, Talha
• Is Part Of: Journal of network and computer applications. Volume 133(2019)
• Page Start: 39
• Page End: 50
• Publication Date: 2019-05-01
• Subjects: Clustering impact coefficient -- Mobile social network -- Node's willingness and influence -- Weighted degree -- Temporal evolution graph model -- Time-aggregated graph model -- Susceptible-infected-recovered (SIR); 00-01 -- 99-00; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2019.02.012
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: In mobile social networks, extracting the most powerful individuals to disseminate information in the network is attracting the attention of many researchers. Identifying influential nodes in Mobile Social Networks (MSNs) helps to increase the efficiency of bandwidth in wireless communication by leveraging cellular links to device-to-device communications. Recently, numerous techniques have been proposed from different perspectives, each with its particular advantages and weaknesses. In this paper, Node Willingness and Influence (NWI) algorithm is proposed to extract the most powerful spreaders in 5G MSNs, which considers the willingness and influence of the node to disseminate information in the network through its neighbors and 2-step neighbors. Firstly, the Influence (In) of a node is calculated based on the nodes' willingness to share contents with others. Then, based on node degree and neighbors node degree; and nodes strength and neighbors node strength, the Weighted Strength Degree (WSD) and the Clustering Impact Coefficient (CIC) of a node is determined. Finally, the importance of a node based on its willingness to propagate information in the network is done by accounting the Influence (In) and CIC of the node. The temporal evolution graph and time-aggregated graph models are used to capture the topology dynamics of the mobile social networks. Also, Susceptible-Infected-Recovered (SIR) model is used to evaluate the performance of NWI to disseminate information in real-world networks. Results show the effectiveness of the proposed method to extract important nodes in MSNs.