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This paper is concerned with distributed detection of central nodes incomplex networks using closeness centrality. Closeness centrality plays anessential role in network analysis. Evaluating closeness centrality exactlyrequires complete knowledge of the network; for large networks, this may beinefficient, so closeness centrality should be approximated. Distributed taskssuch as leader election can make effective use of centrality information forhighly central nodes, but complete network information is not locallyavailable. This paper refines a distributed centrality computation algorithm byYou et al. [24] by pruning nodes which are almost certainly not most central.For example, in a large network, leave nodes can not play a central role. Thisleads to a reduction in the number of messages exchanged to determine thecentrality of the remaining nodes. Our results show that our approach reducesthe number of messages for networks which contain many prunable nodes. Ourresults also show that reducing the number of messages may

Distributed Identification of Central Nodes with Less Communication