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dc.contributor.authorOdeyomi, Olusola T.
dc.contributor.authorKwon, Hyuck M.
dc.contributor.authorMurrell, David A.
dc.date.accessioned2020-04-30T21:36:46Z
dc.date.available2020-04-30T21:36:46Z
dc.date.issued2020-03-30
dc.identifier.citationO. T. Odeyomi, H. M. Kwon and D. A. Murrell, "Time-Varying Truth Prediction in Social Networks Using Online Learning," 2020 International Conference on Computing, Networking and Communications (ICNC), Big Island, HI, USA, 2020, pp. 171-175en_US
dc.identifier.isbn978-172814905-9
dc.identifier.urihttps://doi.org/10.1109/ICNC47757.2020.9049827
dc.identifier.urihttps://soar.wichita.edu/handle/10057/17529
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractThis paper shows how agents in a social network can predict their true state when the true state is arbitrarily time-varying. We model the social network using graph theory, where the agents are all strongly connected. We then apply online learning and propose a non-stochastic multi-armed bandit algorithm. We obtain a sublinear upper bound regret and show by simulation that all agents can make a better prediction over time.en_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.ispartofseriesInternational Conference on Computing, Networking and Communications (ICNC);2020
dc.subjectGraph theoryen_US
dc.subjectMultiarmed banditen_US
dc.subjectNon-Bayesian learningen_US
dc.subjectOnline learningen_US
dc.subjectRegreten_US
dc.subjectStrongly connected networken_US
dc.titleTime-varying truth prediction in social networks using online learningen_US
dc.typeConference paperen_US
dc.rights.holder© 2020 IEEEen_US


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