  This paper is concerned with distributed detection of central nodes in
complex networks using closeness centrality. Closeness centrality plays an
essential role in network analysis. Evaluating closeness centrality exactly
requires complete knowledge of the network; for large networks, this may be
inefficient, so closeness centrality should be approximated. Distributed tasks
such as leader election can make effective use of centrality information for
highly central nodes, but complete network information is not locally
available. This paper refines a distributed centrality computation algorithm by
You 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. This
leads to a reduction in the number of messages exchanged to determine the
centrality of the remaining nodes. Our results show that our approach reduces
the number of messages for networks which contain many prunable nodes. Our
results also show that reducing the number of messages may
