Distributed Averaging in Population Protocols
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We consider two simple asynchronous opinion dynamics on arbitrary graphs where each node u of the graph has an initial value ξ_u(0). In the first process, the NodeModel, at each time step t≥ 0, a random node u and a random sample of k of its neighbours v_1,v_2,⋯,v_k are selected. Then u updates its current value ξ_u(t) to ξ_u(t+1)=αξ_u(t)+(1-α)/k∑_i=1^kξ_v_i(t), where α∈(0,1) and k≥1 are parameters of the process. In the second process, the EdgeModel, at each step a random edge (u,v) is selected. Node u 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∑_u∈ Vξ_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 load does not depend on the number of nodes, the variance is negligible and 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_ε needed to make all node values `ε-close' to each other. Our bounds are asymptotically tight under some assumptions on the distribution of the starting values.
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