Finding Best Matching Community for Common Nodes in Mobile Social Networks
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Finding Best Matching Community for Common Nodes in Mobile Social Networks Muluneh Mekonnen Tulu1,2 · Ronghui Hou1 · Shambel Aregay Gerezgiher1 · Talha Younas3 · Melkamu Deressa Amentie1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords External density · Mobile community detection · Mobile social networks · Modularity measure
1 Introduction Mobile communication technology has evolved drastically and experienced significant changes to communicate people in different ways. The number and data processed by smartphones will be expected to increase extremely from 2015 to 2020 as per Cisco visual networking prediction. Especially, the mobile video data traffic is expected to increase by 75% [1]. So, to properly handle this big data and to provide a sounding solution for the exploding mobile traffic demand, researchers have continued searching solutions for it. Mobile social network (MSN) has been considered as one promising research area to leverage the current and future cellular network traffic challenges. In mobile social * Muluneh Mekonnen Tulu [email protected]; [email protected] Extended author information available on the last page of the article
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network, there is no exact definition given for community (or cluster). Subramani et al. in [2] defined community as a collection of nodes which have a more common interest within the same group and have less interest with nodes out of the group. In such clustering, node is considered as a user while the connection between two nodes is considered as a link. Moreover, users create local communication among each other through WiFi or Bluetooth for content sharing. Meanwhile, it is worth to mention a couple of advantages of clustering before we look at the detection mechanisms. First and foremost, it creates ea
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