  Community detection emerges as an important task in the discovery of network
mesoscopic structures. However, the concept of a "good" community is very
context-dependent and it is relatively complicated to deduce community
characteristics using available community detection techniques. In reality, the
existence of a gap between structural goodness quality metrics and expected
topological patterns creates a confusion in evaluating community structures. In
this paper, we introduce an empirical multivariate analysis of different
structural goodness properties in order to characterize several detectable
community topologies. Specifically, we show that a combination of two
representative structural dimensions including community transitivity and hub
dominance allows to distinguish different topologies such as star-based,
clique-based, string-based and grid-based structures. Additionally, these
classes of topology disclose structural proximities with those of graphs
created by Erd\H{o}s-R\'{e}nyi, Watts-Strogatz and Barab\'{a}si-Albert
generative models. We illustrate popular community topologies identified by
different detection methods on a large dataset composing many network
categories and associate their structures with the most related graph
generative model. Interestingly, this conjunctive representation sheds light on
fundamental differences between mesoscopic structures in various network
categories including: communication, information, biological, technological,
social, ecological, synthetic networks and more.
