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.
Similar content being viewed by others
References
Fan J, Chen J, Du Y, Wang P, Sun Y (2011) Delque: a socially-aware delegation query scheme in delay tolerant networks. IEEE Trans Veh Technol 60(5):2181–2193
Zhang D, Zhang D, Xiong H, Hsu C, Vasilakos A (2014) BASA: building mobile ad-hoc social networks on top of android. IEEE Netw 28(1):4–9
Yu Q, Chen J, Fan Y, Shen X, Sun Y (2010) Multi-channel assignment in wireless sensor networks: a game theoretic approach. In: Proceedings of IEEE INFOCOM, pp 1–9
He J, Cheng P, Chen J, Shi L, Lu R (2014) Time synchronization for random mobile sensor networks. IEEE Trans Veh Technol 63(8):3935–3946
Zhou H, Chen J, Zheng H, Wu J (2016) Energy efficiency and contact opportunities tradeoff in opportunistic mobile networks. IEEE Trans Veh Technol 65(5):3723–3734
Zhao D, Ma H, Tang S, Li X (2015) Coupon: a cooperative framework for building sensing maps in mobile opportunistic networks. IEEE Trans Parallel Distrib Syst 26(2):392–402
Li F, Wu J (2009) MOPS: providing content-based service in disruption-tolerant networks. In: Proceedings of IEEE ICDCS
Wang Z, Liao J, Cao Q, Qi H, Wang Z (2015) Friendbook: a semantic-based friend recommendation system for social networks. IEEE Trans Mob Comput 29(4):40–45
Zhou H, Chen J, Zhao H, Gao W, Cheng P (2013) On exploiting contact patterns for data forwarding in duty-cycle opportunistic mobile networks. IEEE Trans Veh Technol 62(9):4629–4642
Yuan Q, Cardei I, Wu J (2009) Predict and relay: an efficient routing in disruption-tolerant networks. In: Proceedings of ACM Mobihoc, pp 95–104
Chen H, Lou W (2016) Contact expectation based routing for delay tolerant networks. Ad Hoc Netw 36:244–257
Chen H, Lou W (2014) Gar: Group aware cooperative routing protocol for resource-constraint opportunistic networks. Comput Commun 48:20–29
Scott J (1988) Social network analysis. Sociology 22(1):109–127
Gao W, Li Q, Zhao B, Cao G (2009) Multicasting in delay tolerant networks: a social network perspective. In: Proceedings of ACM Mobihoc. ACM, pp 299–308
Fan J, Chen J, Du Y, Gao W, Wu J, Sun Y (2013) Geo-community-based broadcasting for data dissemination in mobile social networks. IEEE Trans Parallel Distrib Syst 24(4):734–743
Wang S, Huang L, Hsu C, Yang F (2016) Collaboration reputation for trustworthy web service selection in social networks. J Comput Syst Sci 82(1):130–143
Hui P, Chaintreau A, Scott J, Gass R, Crowcroft J, Diot C (2005) Pocket switched networks and human mobility in conference environments. In: Proceedings of the ACM SIGCOMM workshop on Delay-tolerant networking. ACM, pp 244–251
Zhou H, Chen J, Fan J, Du Y, Das SK (2013) ConSub: incentive-based content subscribing in selfish opportunistic mobile networks. IEEE J Sel Areas Commun 31(9):669–679
Zhou H, Wu J, Zhao H, Tang S, Chen C, Chen J (2015) Incentive-driven and freshness-aware content dissemination in selfish opportunistic mobile networks. IEEE Trans Parallel Distrib Syst 26(9):2493–2505
Zhao H, Zhou H, Yuan C, Huang Y, Chen J (2015) Social discovery: exploring the correlation among three-dimensional social relationships. IEEE Trans Comput Soc Syst 2(3):77–87
Daly EM, Haahr M (2007) Social network analysis for routing in disconnected delay-tolerant manets. In: Proceedings of ACM MobiHoc, pp 32–40
Hui P, Crowcroft J, Yoneki E (2008) Bubble rap: social-based forwarding in delay tolerant networks. In: Proceedings of ACM MobiHoc, pp 241–250
Socievole A, De Rango F (2015) Energy-aware centrality for information forwarding in mobile social opportunistic networks. In: IEEE IWCMC. IEEE, pp 622–627
Zhu Y, Zhang C, Mao X, Wang Y (2015) Social based throwbox placement schemes for large-scale mobile social delay tolerant networks. Comput Commun 65:10–26
Chaintreau A, Hui P, Crowcroft J, Diot C, Gass R, Scott J (2007) Impact of human mobility on opportunistic forwarding algorithms. IEEE Trans Mob Comput 6(6):606–620
Karagiannis T, Le Boudec J, Vojnović M (2010) Power law and exponential decay of intercontact times between mobile devices. IEEE Trans Mob Comput 9(10):1377–1390
Passarella A, Conti M (2011) Characterising aggregate inter-contact times in heterogeneous opportunistic networks. In: NETWORKING 2011. Springer, Berlin, pp 301–313
Tang J, Kim H, Anderson R (2012) Temporal node centrality in complex networks. Phys Rev E 85(2):026107
Kim H, Tang J, Anderson R, Mascolo C (2012) Centrality prediction in dynamic human contact networks. Comput Netw 56(3):983–996
Zhou H, Xu S, Huang C (2015) Temporal centrality prediction in opportunistic mobile social networks. In: Hsu C-H, Xia F, Liu X, Wang S (eds) Internet of vehicles-safe and intelligent mobility. Springer, Berlin, pp 68–77
Huang D, Zhang S, Hui P, Chen Z (2015) Link pattern prediction in opportunistic networks with kernel regression. In: International conference on communication systems and networks, IEEE, pp 1–8
Wei W, Carley K (2015) Measuring temporal patterns in dynamic social networks. ACM Trans Knowl Discov Data 10(1):9
Scott J, Gass R, Crowcroft J, Hui P, Diot C, Chaintreau A (2009) Crawdad data set cambridge/haggle (v. 2009-05-29)
Eagle N, Pentland AS, Lazer D (2009) Inferring friendship network structure by using mobile phone data. Proc Natl Acad Sci 106(36):15274–15278. doi:10.1073/pnas.0900282106
Acknowledgments
This research was supported in part by NSFC under Grants 61602272, 61503147, and 41172298, the Open Research Project of State Key Laboratory of Synthetical Automation for Process Industries under Grant PAL-N201507, and Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering under Grant 2014KLA07.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhou, H., Tong, L., Xu, S. et al. Predicting temporal centrality in Opportunistic Mobile Social Networks based on social behavior of people. Pers Ubiquit Comput 20, 885–897 (2016). https://doi.org/10.1007/s00779-016-0958-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00779-016-0958-0