  Temporal closeness is a generalization of the classical closeness centrality
measure for analyzing evolving networks. The temporal closeness of a vertex $v$
is defined as the sum of the reciprocals of the temporal distances to the other
vertices. Ranking all vertices of a network according to the temporal closeness
is computationally expensive as it leads to a single-source-all-destination
(SSAD) temporal distance query starting from each vertex of the graph. To
reduce the running time of temporal closeness computations, we introduce an
index to speed up SSAD temporal distance queries called Substream index. We
show that deciding if a Substream index of a given size exists is NP-complete
and provide an efficient greedy approximation. Moreover, we improve the running
time of the approximation using min-hashing and parallelization. Our evaluation
with real-world temporal networks shows a running time improvement of up to one
order of magnitude compared to the state-of-the-art temporal closeness ranking
algorithms.
