Predicting temporal centrality in Opportunistic Mobile Social Networks based on social behavior of people

Huan Zhou, Linping Tong, Shouzhi Xu, Chung-Ming Huang, Jialu Fan

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Predicting the centrality of nodes is a significant problem for different applications in Opportunistic Mobile Social Networks (OMSNs). However, when calculating such metrics, current studies focused on analyzing static networks that do not change over time or using aggregated contact information over a period of time. Furthermore, the centrality measured in the past is not verified whether it is useful as a predictor for the future. In this paper, in order to capture the dynamic behavior of people, we focus on predicting nodes’ future centrality (importance) from the temporal perspective using real mobility traces in OMSNs. Three important centrality metrics, namely betweenness, closeness, and degree centrality, are considered. Through real trace-driven simulations, we find that nodes’ future centrality is highly predictable due to natural social behavior of people. 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 Weighted Average Method performs best in the MIT Reality trace, and the recent Uniform Average Method performs best in the Infocom 06 trace. Furthermore, we also evaluate the impact of parameters m and w on the performance of the proposed methods and find proper values of different parameters for each proposed method at the same time.

Original languageEnglish
Pages (from-to)885-897
Number of pages13
JournalPersonal and Ubiquitous Computing
Volume20
Issue number6
DOIs
Publication statusPublished - 2016 Nov 1

Fingerprint

Social networks
Centrality
Node
Simulation
Predictors
Prediction
Closeness
Betweenness

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Science Applications
  • Management Science and Operations Research

Cite this

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abstract = "Predicting the centrality of nodes is a significant problem for different applications in Opportunistic Mobile Social Networks (OMSNs). However, when calculating such metrics, current studies focused on analyzing static networks that do not change over time or using aggregated contact information over a period of time. Furthermore, the centrality measured in the past is not verified whether it is useful as a predictor for the future. In this paper, in order to capture the dynamic behavior of people, we focus on predicting nodes’ future centrality (importance) from the temporal perspective using real mobility traces in OMSNs. Three important centrality metrics, namely betweenness, closeness, and degree centrality, are considered. Through real trace-driven simulations, we find that nodes’ future centrality is highly predictable due to natural social behavior of people. 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 Weighted Average Method performs best in the MIT Reality trace, and the recent Uniform Average Method performs best in the Infocom 06 trace. Furthermore, we also evaluate the impact of parameters m and w on the performance of the proposed methods and find proper values of different parameters for each proposed method at the same time.",
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Predicting temporal centrality in Opportunistic Mobile Social Networks based on social behavior of people. / Zhou, Huan; Tong, Linping; Xu, Shouzhi; Huang, Chung-Ming; Fan, Jialu.

In: Personal and Ubiquitous Computing, Vol. 20, No. 6, 01.11.2016, p. 885-897.

Research output: Contribution to journalArticle

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