Abstract
In this paper, we focus on predicting nodes’ future importance under three important metrics, namely betweenness, and closeness centrality, using real mobility traces in Opportunistic Mobile Social Networks (OMSNs). Through real trace-driven simulations, we find that nodes’ importance is highly predictable due to natural social behaviour of human. Then, based on the observations in the simulation, we design several reasonable prediction methods to predict nodes’ future temporal centrality. Finally, extensive real trace-driven simulations are conducted to evaluate the performance of our proposed methods. The results show that the Recent Uniform Average method performs best when predicting the future Betweenness centrality, and the Periodical Average Method performs best when predicting the future Closeness centrality in the MIT Reality trace. Moreover, the Recent Uniform Average method performs best in the Infocom 06 trace.
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Acknowledgements
This research was supported in part by NSFC under grants 61174177, Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering under grant 2014KLA07, and Natural Science Foundation of Hubei Province of China under grant 2014CFB145. The corresponding author is Shouzhi Xu, Email: xsz@ctgu.edu.cn
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Zhou, H., Xu, S., Huang, C. (2015). Temporal Centrality Prediction in Opportunistic Mobile Social Networks. In: Hsu, CH., Xia, F., Liu, X., Wang, S. (eds) Internet of Vehicles - Safe and Intelligent Mobility. IOV 2015. Lecture Notes in Computer Science(), vol 9502. Springer, Cham. https://doi.org/10.1007/978-3-319-27293-1_7
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DOI: https://doi.org/10.1007/978-3-319-27293-1_7
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