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Finding Best Matching Community for Common Nodes in Mobile Social Networks

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Abstract

The increase of mobile data users has created traffic congestion in current cellular networks. Due to this, mobile network providers have been facing difficulty in delivering the best services for customers. Since, detecting community in mobile social network is a valuable technique to leverage the downlink traffic congestion by enhancing local communications within the community, it attracts the attention of many researchers. Therefore, developing an algorithm, which detects community, plays a key role in mobile social network. In this paper, first, we proposed external density metrics to detect mobile social network. External density is defined as the ratio of outgoing links to total links of the community. Second, method to find the best group for common node is proposed. Therefore, an external density algorithm, makes a fair partition by grouping common nodes to a community with relatively higher external density. As a result, the overall modularity value of the network has increased. Third, the proposed algorithm is evaluated. Hence, the evaluation results confirm that our proposed approach has demonstrated good performance improvements than traditional methods.

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Author notes

  1. Muluneh Mekonnen Tulu

    Present address: Department of Electrical and Computer Engineering, College of Electrical and Mechanical Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia

Authors and Affiliations

  1. State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi’an, 710071, Shaanxi, People’s Republic of China

    Muluneh Mekonnen Tulu, Ronghui Hou, Shambel Aregay Gerezgiher & Melkamu Deressa Amentie

  2. COMSATS University Sahiwal, Sahiwal, Pakistan

    Talha Younas

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Tulu, M.M., Hou, R., Gerezgiher, S.A. et al. Finding Best Matching Community for Common Nodes in Mobile Social Networks. Wireless Pers Commun 114, 2889–2908 (2020). https://doi.org/10.1007/s11277-020-07508-7

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