  Networks in nature possess a remarkable amount of structure. Via a series of
data-driven discoveries, the cutting edge of network science has recently
progressed from positing that the random graphs of mathematical graph theory
might accurately describe real networks to the current viewpoint that networks
in nature are highly complex and structured entities. The identification of
high order structures in networks unveils insights into their functional
organization. Recently, Clauset, Moore, and Newman, introduced a new algorithm
that identifies such heterogeneities in complex networks by utilizing the
hierarchy that necessarily organizes the many levels of structure. Here, we
anchor their algorithm in a general community detection framework and discuss
the future of community detection.
