Truth Prediction by Weakly Connected Agents in Social Networks Using Online Learning

Abstract

This paper provides a study into the social network where influential personalities collaborate positively among themselves to learn an underlying truth over time but may have misled their followers to believe a false information. Most existing work models the social network as a graph network and applies non-Bayesian learning to understand the behavior of agents in the network. Although this approach is popular, it has the limitation of assuming that the truth - otherwise called the true state - is time-invariant. This is not practical in social network where streams of information are released and updated every second. Thus, this paper improves on existing work by introducing online reinforcement learning into the graph theoretic framework. Specifically, multi-armed bandit technique is applied. A multi-armed bandit algorithm is proposed for weakly connected agents to predict the time-varying true state. The result shows that the weakly connected agents can predict this time-varying true state, howbeit with a higher regret than the strongly connected agents.