  Community detection in social networks is a problem with considerable
interest, since, discovering communities reveals hidden information about
networks. There exist many algorithms to detect inherent community structures
and recently few of them are investigated on social networks. However, it is
non-trivial to decide the best approach in the presence of diverse nature of
graphs, in terms of density and sparsity, and inadequate analysis of the
results. Therefore, in this study, we analyze and compare various algorithms to
detect communities in two networks, namely social and road networks, with
varying structural properties. The algorithms under consideration are evaluated
with unique metrics for internal and external connectivity of communities that
includes internal density, average degree, cut ratio, conductance, normalized
cut, and average Jaccard Index. The evaluation results revealed key insights
about selected algorithms and underlying community structures.
