  One of the most widely studied problem in mining and analysis of complex
networks is the detection of community structures. The problem has been
extensively studied by researchers due to its high utility and numerous
applications in various domains. Many algorithmic solutions have been proposed
for the community detection problem but the quest to find the best algorithm is
still on. More often than not, researchers focus on developing fast and
accurate algorithms that can be generically applied to networks from a variety
of domains without taking into consideration the structural and topological
variations in these networks.
  In this paper, we evaluate the performance of different clustering algorithms
as a function of varying network topology. Along with the well known LFR model
to generate benchmark networks with communities,we also propose a new model
named Naive Scale Free Model to study the behavior of community detection
algorithms with respect to different topological features. More specifically,
we are interested in the size of networks, the size of community structures,
the average connectivity of nodes and the ratio of inter-intra cluster edges.
Results reveal several limitations of the current popular network clustering
algorithms failing to correctly find communities. This suggests the need to
revisit the design of current clustering algorithms that fail to incorporate
varying topological features of different networks.
