Abstract
The topic of community detection in social networks has attracted a lot of attention in recent years. Existing methods always depict the relationship of two nodes using the snapshot of the network, but these snapshots cannot reveal the real relationships, especially when the connection history among nodes is considered. The problem of detecting the stable community in mobile social networks has been studied in this paper. Community cores are considered as stable subsets of the network in previous work. Based on these observations, this paper divides all nodes into a few of communities due to the community cores. Meanwhile, communities can be tracked through incremental computing. Experimental results based on real-world social networks demonstrate that our proposed method performs better than the well-known static community detection algorithm in mobile social networks.
Highlights
IntroductionThe way people communicate has experienced dramatic changes. Thanks to the development of mobile communication technology, the relative geographical topology of people can be determined
In recent years, the way people communicate has experienced dramatic changes
We firstly discuss the stability of community cores which are detected by our algorithms (5.1–5.4)
Summary
The way people communicate has experienced dramatic changes. Thanks to the development of mobile communication technology, the relative geographical topology of people can be determined. The static approaches focus on high aggregation of nodes which have same features [1,2], while the dynamic approaches divide the network's evolving process into a few of timestamps, paying attention to the degree of aggregation, and to the computational complexity at each timestamp [3,4]. Few of these methods consider the stability of communities between two timestamps. Seifi [5] considered the stability in community detection, but tried to obtain a community partition in a stable modularity scenario, rather than stable contact
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