  Assessing the partitioning performance of community detection algorithms is
one of the most important issues in complex network analysis. Artificially
generated networks are often used as benchmarks for this purpose. However,
previous studies showed their level of realism have a significant effect on the
algorithms performance. In this study, we adopt a thorough experimental
approach to tackle this problem and investigate this effect. To assess the
level of realism, we use consensual network topological properties. Based on
the LFR method, the most realistic generative method to date, we propose two
alternative random models to replace the Configuration Model originally used in
this algorithm, in order to increase its realism. Experimental results show
both modifications allow generating collections of community-structured
artificial networks whose topological properties are closer to those
encountered in real-world networks. Moreover, the results obtained with eleven
popular community identification algorithms on these benchmarks show their
performance decrease on more realistic networks.
