Fast Graphical Population Protocols
Dan Alistarh, Rati Gelashvili, Joel Rybicki

TL;DR
This paper introduces a method to simulate population protocols designed for complete networks on arbitrary regular graphs, enabling efficient leader election and majority tasks on graphs with good expansion properties.
Contribution
It presents a simulation technique that translates protocols from complete graphs to regular graphs with polylogarithmic overhead, leveraging conductance for efficiency.
Findings
Leader election and majority solved in ^{-2} n polylog n interactions
Simulation overhead is polylogarithmic in n and quadratic in conductance
Protocols are space-efficient with ^{-2} imes polylog n states per node
Abstract
Let be a graph on nodes. In the stochastic population protocol model, a collection of indistinguishable, resource-limited nodes collectively solve tasks via pairwise interactions. In each interaction, two randomly chosen neighbors first read each other's states, and then update their local states. A rich line of research has established tight upper and lower bounds on the complexity of fundamental tasks, such as majority and leader election, in this model, when is a clique. Specifically, in the clique, these tasks can be solved fast, i.e., in pairwise interactions, with high probability, using at most states per node. In this work, we consider the more general setting where is an arbitrary graph, and present a technique for simulating protocols designed for fully-connected networks in any connected regular…
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