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We present a new and efficient algorithm for determining a consensus valuefor a network of agents. Different from existing algorithms, our algorithmevaluates the consensus value for very large networks using heat kernelpagerank. We consider two frameworks for the consensus problem, a weightedaverage consensus among all agents, and consensus in a leader-followingformation. Using a heat kernel pagerank approximation, we give consensusalgorithms that run in time sublinear in the size of the network, and providequantitative analysis of the tradeoff between performance guarantees and errorestimates.

Finding Consensus in Multi-Agent Networks Using Heat Kernel Pagerank