  Network science plays an increasingly important role to model complex data in
many scientific disciplines. One notable feature of network organization is
community structure, which refers to clusters of tightly interconnected nodes.
A prominent problem is how to investigate the relationship between macro-scale
modules that are retrieved by optimizing global network measures, and
micro-scale structure that are defined by specific queries of the analysis
(e.g., nodal features). By generalizing fundamental concepts of joint
space-frequency localization to network theory, here we propose a flexible
framework to study interactions between micro- and macro-structure. Similar to
pointing and focusing a magnifying glass, the analysis can be directed to
specific micro-scale structure, while the degree of interaction with the
macro-scale community structure can be seamlessly controlled. In addition, the
method is computationally efficient as a result of the underlying
low-dimensional optimization problem.
