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 duke waves and fades 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 jacques marie mage baudelaire 2 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.