  This paper is concerned with distributed computation of several commonly used
centrality measures in complex networks. In particular, we propose
deterministic algorithms, which converge in finite time, for the distributed
computation of the degree, closeness and betweenness centrality measures in
directed graphs. Regarding eigenvector centrality, we consider the PageRank
problem as its typical variant, and design distributed randomized algorithms to
compute PageRank for both fixed and time-varying graphs. A key feature of the
proposed algorithms is that they do not require to know the network size, which
can be simultaneously estimated at every node, and that they are clock-free. To
address the PageRank problem of time-varying graphs, we introduce the novel
concept of persistent graph, which eliminates the effect of spamming nodes.
Moreover, we prove that these algorithms converge almost surely and in the
sense of $L^p$. Finally, the effectiveness of the proposed algorithms is
illustrated via extensive simulations using a classical benchmark.
