  Identifying community structure can be a potent tool in the analysis and
understanding of the structure of complex networks. Up to now, methods for
evaluating the performance of identification algorithms use ad-hoc networks
with communities of equal size. We show that inhomogeneities in community sizes
can and do affect the performance of algorithms considerably, and propose an
alternative method which takes these factors into account. Furthermore, we
propose a simple modification of the algorithm proposed by Newman for community
detection (Phys. Rev. E {\bf 69} 066133) which treats communities of different
sizes on an equal footing, and show that it outperforms the original algorithm
while retaining its speed.
