  Datasets of real-world applications are characterized by entities of
different types, which are defined by multiple features and connected via
varied types of relationships. A critical challenge for these datasets is
developing models and computations to support flexible analysis, i.e., the
ability to compute varied types of analysis objectives in an efficient manner.
  To address this problem, in this paper, we make a case for modeling such
complex data sets as multilayer networks (or MLNs), and argue that MLNs provide
a more informative model than the currently popular simple and attribute
graphs. Through analyzing communities and hubs on homogeneous and heterogeneous
MLNs, we demonstrate the flexibility of the chosen model. We also show that
compared to current analysis approaches, a network decoupling-based analysis of
MLNs is more efficient and also preserves the structure and result semantics.
  We use three diverse data sets to showcase the effectiveness of modeling them
as MLNs and analyzing them using the decoupling-based approach. We use both
homogeneous and heterogeneous MLNs for modeling and community and hub
computations for analysis. The data sets are from US commercial airlines and
IMDb, a large international movie data set. Our experimental analysis validate
modeling, efficiency of computation, and versatility of the approach.
Correctness of results are verified using independently available ground truth.
For the data sets used, efficiency improvement is in the range of 64% to 98%.
