  How can we accurately compare different community detection algorithms? These
algorithms cluster nodes in a given network, and their performance is often
validated on benchmark networks with explicit ground-truth communities. Given
the lack of cluster labels in real-world networks, a model that generates
realistic networks is required for accurate evaluation of these algorithm. In
this paper, we present a simple, intuitive, and flexible benchmark generator to
generate intrinsically modular networks for community validation. We show how
the generated networks closely comply with the characteristics observed for
real networks; whereas their characteristics could be directly controlled to
match wide range of real world networks. We further show how common community
detection algorithms rank differently when being evaluated on these benchmarks
compared to current available alternatives.
