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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.