Voter model on networks partitioned into two cliques of arbitrary sizes

The voter model is an archetypal stochastic process that represents opinion dynamics. In each update, one agent is chosen uniformly at random. The selected agent then copies the current opinion of a randomly selected neighbour. We investigate the voter model on a network with an exogenous community structure: two cliques (i.e. complete subgraphs) randomly linked by X interclique edges. We show that, counterintuitively, the mean consensus time is typically not a monotonically decreasing function of X. Cliques of fixed proportions with opposite initial opinions reach a consensus, on average, most quickly if X scales as N-3/2, where N is the number of agents in the network. Hence, to accelerate a consensus between cliques, agents should connect to more members in the other clique as N increases but not to the extent that cliques lose their identity as distinct communities. We support our numerical results with an equation-based analysis. By interpolating between two asymptotic heterogeneous mean-field approximations, we obtain an equation for the mean consensus time that is in excellent agreement with simulations for all values of X.