Time-Varying Truth Prediction in Social Networks Using Online Learning

Abstract

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.