  This paper is an extensive survey of literature on complex network
communities and clustering. Complex networks describe a widespread variety of
systems in nature and society especially systems composed by a large number of
highly interconnected dynamical entities. Complex networks like real networks
can also have community structure. There are several types of methods and
algorithms for detection and identification of communities in complex networks.
Several complex networks have the property of clustering or network
transitivity. Some of the important concepts in the field of complex networks
are small-world and scale-robustness, degree distributions, clustering, network
correlations, random graph models, models of network growth, dynamical
processes on networks, etc. Some current areas of research on complex network
communities are those on community evolution, overlapping communities,
communities in directed networks, community characterization and
interpretation, etc. Many of the algorithms or methods proposed for network
community detection through clustering are modified versions of or inspired
from the concepts of minimum-cut based algorithms, hierarchical connectivity
based algorithms, the original GirvanNewman algorithm, concepts of modularity
maximization, algorithms utilizing metrics from information and coding theory,
and clique based algorithms.
