Effect of size heterogeneity on community identification in complex   networks
  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. 
Leon Danon, Albert Diaz-Guilera and Alex Arenas