  Because networks can be used to represent many complex systems, they have
attracted considerable attention in physics, computer science, sociology, and
many other disciplines. One of the most important areas of network science is
the algorithmic detection of cohesive groups (i.e., "communities") of nodes. In
this paper, we algorithmically detect communities in social networks and image
data by optimizing multislice modularity. A key advantage of modularity
optimization is that it does not require prior knowledge of the number or sizes
of communities, and it is capable of finding network partitions that are
composed of communities of different sizes. By optimizing multislice modularity
and subsequently calculating diagnostics on the resulting network partitions,
it is thereby possible to obtain information about network structure across
multiple system scales. We illustrate this method on data from both social
networks and images, and we find that optimization of multislice modularity
performs well on these two tasks without the need for extensive
problem-specific adaptation. However, improving the computational speed of this
method remains a challenging open problem.
