  Networks (or graphs) are used to model the dyadic relations between entities
in a complex system. In cases where there exists multiple relations between the
entities, the complex system can be represented as a multilayer network, where
the network in each layer represents one particular relation (or feature). The
analysis of multilayer networks involves combining edges from specific layers
and then computing a network property.
  Different subsets of the layers can be combined. For any Boolean combination
operation (e.g. AND, OR), the number of possible subsets is exponential to the
number of layers. Thus recomputing for each subset from scratch is an expensive
process. In this paper, we propose to efficiently analyze multilayer networks
using a method that we term network decomposition.
  Network decomposition is based on analyzing each network layer individually
and then aggregating the analysis results. We demonstrate the effectiveness of
using network decomposition for detecting communities on different combinations
of network layers. Our results on multilayer networks obtained from real-world
and synthetic datasets show that our proposed network decomposition method
requires significantly lower computation time while producing results of high
accuracy.
