We study the problem of determining the majority type in an arbitrary connected network, each vertex of which has initially two possible types. The vertices may later change into other types, out of a set of a few additional possible types, and can interact in pairs only if they share an edge. Any (population) protocol is required to stabilize in the initial majority. First we prove that there does not exist any population protocol that always computes majority in any interaction graph A preliminary conference version of this work appeared in the 41st International Colloquium on Automata, Languages, and Programming (ICALP), Copenhagen, Denmark, 2014 [18]. George B. Mertzios was partially supported by the EPSRC Grant EP/K022660/1. Sotiris E. Nikoletseas was partially supported by the MULTIPLEX project-317532. Christoforos L. Raptopoulos was partially supported by the SHARPEN project-PE6 (1081). Paul G. Spirakis was partially supported by the MULTIPLEX project-317532, the EU ERC Project ALGAME and the EEE/CS School of the University of Liverpool. by using at most 3 types per vertex. However this does not rule out the existence of a protocol with 3 types per vertex that is correct with high probability (whp). To this end, we examine an elegant and very natural majority protocol with 3 types per vertex, introduced in Angluin et al. (Distrib. Computing 21(2):87-102, 2008), whose performance has been analyzed for the clique graph. In particular, we study the performance of this protocol in arbitrary networks, under the probabilistic scheduler. We prove that, if the initial assignement of types to vertices is random, the protocol of Angluin et al. (Distrib. Computing 21(2):87-102, 2008) converges to the initial majority with probability higher than the probability of converging to the initial minority. In contrast, we show that the resistance of the protocol to failure when the underlying graph is a clique causes the failure of the protocol in general graphs.
By contrast, REF presented the first protocol which converges to the initial majority type with probability 1, even when the difference between types in the original population is constant.
Determining majority in networks with local interactions and very small local memory
Distributed Computing
Distributed Computing
Computer Science Engineering