  Centrality measures for simple graphs/networks are well-defined and each has
numerous main-memory algorithms. However, for modeling complex data sets with
multiple types of entities and relationships, simple graphs are not ideal.
Multilayer networks (or MLNs) have been proposed for modeling them and have
been shown to be better suited in many ways. Since there are no algorithms for
computing centrality measures directly on MLNs, existing strategies reduce
(aggregate or collapse) the MLN layers to simple networks using Boolean AND or
OR operators. This approach negates the benefits of MLN modeling as these
computations tend to be expensive and furthermore results in loss of structure
and semantics. In this paper, we propose heuristic-based algorithms for
computing centrality measures (specifically, degree centrality) on MLNs
directly (i.e., without reducing them to simple graphs) using a newly-proposed
decoupling-based approach which is efficient as well as structure and semantics
preserving. We propose multiple heuristics to calculate the degree centrality
using the network decoupling-based approach and compare accuracy and precision
with Boolean OR aggregated Homogeneous MLNs (HoMLN) for ground truth. The
network decoupling approach can take advantage of parallelism and is more
efficient compared to aggregation-based approaches. Extensive experimental
analysis is performed on large synthetic and real-world data sets of varying
characteristics to validate the accuracy and efficiency of our proposed
algorithms.
