  Centrality measures for simple graphs are well-defined and several
main-memory algorithms exist for each. Simple graphs are not adequate for
modeling complex data sets with multiple entities and relationships. Multilayer
networks (MLNs) have been shown to be better suited, but there are very few
algorithms for centrality computation directly on MLNs. They are converted
(aggregated or collapsed) to simple graphs using Boolean AND or OR operators to
compute centrality, which is not only inefficient but incurs a loss of
structure and semantics. In this paper, we propose algorithms that compute
closeness centrality on an MLN directly using a novel decoupling-based
approach. Individual results of layers (or simple graphs) of an MLN are used
and a composition function developed to compute the centrality for the MLN. The
challenge is to do this accurately and efficiently. However, since these
algorithms do not have complete information of the MLN, computing a global
measure such as closeness centrality is a challenge. Hence, these algorithms
rely on heuristics derived from intuition. The advantage is that this approach
lends itself to parallelism and is more efficient compared to the traditional
approach. We present two heuristics for composition and experimentally validate
accuracy and efficiency on a large number of synthetic and real-world graphs
with diverse characteristics.
