A novel mathematical framework for similarity-based opportunistic social networks

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dc.contributor.author ElSherief, Mai
dc.contributor.author Alipour, Babak
dc.contributor.author Al Qathrady, Mimonah
dc.contributor.author ElBatt, Tamer
dc.contributor.author Zahran, Ahmed
dc.contributor.author Helmy, Ahmed
dc.date.accessioned 2018-04-27T11:23:15Z
dc.date.available 2018-04-27T11:23:15Z
dc.date.issued 2017-09-28
dc.identifier.citation ElSherief, M., Alipour, B., Al Qathrady, M., ElBatt, T., Zahran, A. and Helmy, A. (2017) 'A novel mathematical framework for similarity-based opportunistic social networks', Pervasive and Mobile Computing, 42, pp.134-150. doi:10.1016/j.pmcj.2017.08.004 en
dc.identifier.volume 42 en
dc.identifier.startpage 134 en
dc.identifier.endpage 150 en
dc.identifier.issn 1574-1192
dc.identifier.uri http://hdl.handle.net/10468/5895
dc.identifier.doi https://doi.org/10.1016/j.pmcj.2017.08.004
dc.description.abstract In this paper we study social networks as an enabling technology for new applications and services leveraging, largely unutilized, opportunistic mobile encounters. More specifically, we quantify mobile user similarity and introduce a novel mathematical framework, grounded in information theory, to characterize fundamental limits and quantify the performance of sample knowledge sharing strategies. First, we introduce generalized, non-temporal and temporal profile structures, beyond geographic location, as a probability mass function. Second, we examine classic and information-theoretic similarity metrics using data in the public domain. A noticeable finding is that temporal metrics give lower similarity indices on the average (i.e., conservative) compared to non-temporal metrics, due to leveraging the wealth of information in the temporal dimension. Third, we introduce a novel mathematical framework that establishes fundamental limits for knowledge sharing among similar opportunistic users. Finally, we show numerical results quantifying the cumulative knowledge gain over time and its upper bound, the knowledge gain limit, using public smartphone data for the user behavior and mobility traces, in the case of fixed as well as mobile scenarios. The presented results provide valuable insights highlighting the key role of the introduced information-theoretic framework in motivating future research along this ripe research direction, studying diverse scenarios as well as novel knowledge sharing strategies. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Elsevier en
dc.rights © 2017, Elsevier B.V. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license. en
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/ en
dc.subject Social networks en
dc.subject Opportunistic en
dc.subject Profiles en
dc.subject Similarity en
dc.subject Modeling en
dc.subject User traces en
dc.subject Numerical results en
dc.title A novel mathematical framework for similarity-based opportunistic social networks en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Ahmed Zahran, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000 Email: ahmed.zahran@ucc.ie en
dc.internal.availability Full text not available en
dc.check.date 2019-09-28
dc.date.updated 2018-04-27T11:02:50Z
dc.description.version Accepted Version en
dc.internal.rssid 435466833
dc.description.status Peer reviewed en
dc.identifier.journaltitle Pervasive and Mobile Computing en
dc.internal.copyrightchecked Yes en
dc.internal.licenseacceptance Yes en
dc.internal.IRISemailaddress ahmed.zahran@ucc.ie en


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© 2017, Elsevier B.V. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license. Except where otherwise noted, this item's license is described as © 2017, Elsevier B.V. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license.
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