Learning the Truth in Social Networks Using Multi-Armed Bandit

Abstract

This paper explains how agents in a social network can learn the arbitrary time-varying true state of the network. This is practical in social networks where information is released and updated without any coordination. Most existing literature for learning the true state using the non-Bayesian learning approach, assumes that this true state is fixed, which is impractical. To address this problem, the social network is modeled as a graph network, and the time-varying true state is treated as a multi-armed bandit problem. The few works that have applied multi-armed bandit to a social network did not take into consideration the adversarial effects. Therefore, this paper proposes two non-stochastic multi-armed bandit algorithms that can handle the time-varying true state, even in the presence of an oblivious adversary. Regret bounds on the algorithms are obtained, and the simulation performance shows that all agents can converge to the most stable state. The sublinearity of the proposed algorithms is also compared with two well-known non-stochastic multi-armed bandit algorithms.