  Detecting and analyzing dense groups or communities from social and
information networks has attracted immense attention over last one decade due
to its enormous applicability in different domains. Community detection is an
ill-defined problem, as the nature of the communities is not known in advance.
The problem has turned out to be even complicated due to the fact that
communities emerge in the network in various forms - disjoint, overlapping,
hierarchical etc. Various heuristics have been proposed depending upon the
application in hand. All these heuristics have been materialized in the form of
new metrics, which in most cases are used as optimization functions for
detecting the community structure, or provide an indication of the goodness of
detected communities during evaluation. There arises a need for an organized
and detailed survey of the metrics proposed with respect to community detection
and evaluation. In this survey, we present a comprehensive and structured
overview of the start-of-the-art metrics used for the detection and the
evaluation of community structure. We also conduct experiments on synthetic and
real-world networks to present a comparative analysis of these metrics in
measuring the goodness of the underlying community structure.
