TY - GEN
T1 - Temporal centrality prediction in opportunistic mobile social networks
AU - Zhou, Huan
AU - Xu, Shouzhi
AU - Huang, Chungming
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84952026135
U2 - 10.1007/978-3-319-27293-1_7
DO - 10.1007/978-3-319-27293-1_7
M3 - 会议稿件
AN - SCOPUS:84952026135
SN - 9783319272924
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 68
EP - 77
BT - Internet of Vehicles – Safe and Intelligent Mobility - 2nd International Conference, IOV 2015, Proceedings
A2 - Xia, Feng
A2 - Hsu, Ching-Hsien
A2 - Liu, Xingang
A2 - Wang, Shangguang
PB - Springer Verlag
T2 - 2nd International Conference on Internet of Vehicles – Safe and Intelligent Mobility, IOV 2015
Y2 - 19 December 2015 through 21 December 2015
ER -