Time-varying truth prediction in social networks using online learning
Odeyomi, Olusola T.
Kwon, Hyuck M.
Murrell, David A.
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O. 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. 171-175
This 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.
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