Temporal centrality prediction in opportunistic mobile social networks

Huan Zhou; Shouzhi Xu; Chungming Huang

doi:10.1007/978-3-319-27293-1_7

Temporal centrality prediction in opportunistic mobile social networks

Huan Zhou, Shouzhi Xu, Chungming Huang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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.

Original language	English
Title of host publication	Internet of Vehicles – Safe and Intelligent Mobility - 2nd International Conference, IOV 2015, Proceedings
Editors	Feng Xia, Ching-Hsien Hsu, Xingang Liu, Shangguang Wang
Publisher	Springer Verlag
Pages	68-77
Number of pages	10
ISBN (Print)	9783319272924
DOIs	https://doi.org/10.1007/978-3-319-27293-1_7
State	Published - 2015
Externally published	Yes
Event	2nd International Conference on Internet of Vehicles – Safe and Intelligent Mobility, IOV 2015 - Chengdu, China Duration: 19 Dec 2015 → 21 Dec 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9502
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Conference on Internet of Vehicles – Safe and Intelligent Mobility, IOV 2015
Country/Territory	China
City	Chengdu
Period	19/12/15 → 21/12/15

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10.1007/978-3-319-27293-1_7

Cite this

Zhou, H., Xu, S., & Huang, C. (2015). Temporal centrality prediction in opportunistic mobile social networks. In F. Xia, C.-H. Hsu, X. Liu, & S. Wang (Eds.), Internet of Vehicles – Safe and Intelligent Mobility - 2nd International Conference, IOV 2015, Proceedings (pp. 68-77). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9502). Springer Verlag. https://doi.org/10.1007/978-3-319-27293-1_7

Zhou, Huan ; Xu, Shouzhi ; Huang, Chungming. / Temporal centrality prediction in opportunistic mobile social networks. Internet of Vehicles – Safe and Intelligent Mobility - 2nd International Conference, IOV 2015, Proceedings. editor / Feng Xia ; Ching-Hsien Hsu ; Xingang Liu ; Shangguang Wang. Springer Verlag, 2015. pp. 68-77 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{9bba98c8ec0f4ea98bdf55860a5d800e,

title = "Temporal centrality prediction in opportunistic mobile social networks",

abstract = "In this paper, we focus on predicting nodes{\textquoteright} 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{\textquoteright} 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{\textquoteright} 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.",

author = "Huan Zhou and Shouzhi Xu and Chungming Huang",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 2nd International Conference on Internet of Vehicles – Safe and Intelligent Mobility, IOV 2015 ; Conference date: 19-12-2015 Through 21-12-2015",

year = "2015",

doi = "10.1007/978-3-319-27293-1_7",

language = "英语",

isbn = "9783319272924",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "68--77",

editor = "Feng Xia and Ching-Hsien Hsu and Xingang Liu and Shangguang Wang",

booktitle = "Internet of Vehicles – Safe and Intelligent Mobility - 2nd International Conference, IOV 2015, Proceedings",

}

Zhou, H, Xu, S & Huang, C 2015, Temporal centrality prediction in opportunistic mobile social networks. in F Xia, C-H Hsu, X Liu & S Wang (eds), Internet of Vehicles – Safe and Intelligent Mobility - 2nd International Conference, IOV 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9502, Springer Verlag, pp. 68-77, 2nd International Conference on Internet of Vehicles – Safe and Intelligent Mobility, IOV 2015, Chengdu, China, 19/12/15. https://doi.org/10.1007/978-3-319-27293-1_7

Temporal centrality prediction in opportunistic mobile social networks. / Zhou, Huan; Xu, Shouzhi; Huang, Chungming.
Internet of Vehicles – Safe and Intelligent Mobility - 2nd International Conference, IOV 2015, Proceedings. ed. / Feng Xia; Ching-Hsien Hsu; Xingang Liu; Shangguang Wang. Springer Verlag, 2015. p. 68-77 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9502).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

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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 -

Zhou H, Xu S, Huang C. Temporal centrality prediction in opportunistic mobile social networks. In Xia F, Hsu CH, Liu X, Wang S, editors, Internet of Vehicles – Safe and Intelligent Mobility - 2nd International Conference, IOV 2015, Proceedings. Springer Verlag. 2015. p. 68-77. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-27293-1_7

Temporal centrality prediction in opportunistic mobile social networks

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