Distributed Averaging in Opinion Dynamics

We consider two simple asynchronous opinion dynamics on arbitrary graphs where every node u of the graph has an initial value xi(u)(0). In the first process, which we call the Nodemodel, at each time step t >= 0, a random node.. and a random sample of k of its neighbours v(1), v(2), . . . , v(k) are selected. Then, u updates its current value xi(u)(t) to xi(u)(t + 1) = alpha xi(u) (t) + (1-alpha)/k Sigma(k)(i-1) xi(vi)(t), where alpha is an element of(0, 1) and k >= 1 are parameters of the process. In the second process, called the. Edgemodel, at each step a random pair of adjacent nodes (u , v) is selected, and then nodeu updates its value equivalently to the Nodemodel with k = 1 and v as the selected neighbour. For both processes, the values of all nodes converge to the same value F, which is a random variable depending on the random choices made in each step. For the Nodemodel and regular graphs, and for the Edgemodel and arbitrary graphs, the expectation of F is the average of the initial values 1/n Sigma(u is an element of V) xi(u)(0). For the Nodemodel and non-regular graphs, the expectation of F is the degree-weighted average of the initial values. Our results are two-fold. We consider the concentration of F and show tight bounds on the variance of F for regular graphs. We show that when the initial values do not depend on the number of nodes, then the variance is negligible, and hence the nodes are able to estimate the initial average of the node values. Interestingly, this variance does not depend on the graph structure. For the proof we introduce a duality between our processes and a process of two correlated random walks. We also analyse the convergence time for both models and for arbitrary graphs, showing bounds on the time T-epsilon required to make all node values 'epsilon-close' to each other. Our bounds are asymptotically tight under some assumptions on the distribution of the initial values.