Statistical inference of assortative community structures
This work addresses the challenge of accurately detecting assortative communities in networks for researchers in network science, offering a more reliable alternative to existing methods that often exaggerate assortativity.
The authors tackled the problem of inferring assortative community structures in networks by developing a nonparametric Bayesian method based on the planted partition model, which avoids overfitting and resolution limits unlike alternatives like modularity maximization, and they demonstrated its effectiveness in identifying statistically significant modules in empirical networks.
We develop a principled methodology to infer assortative communities in networks based on a nonparametric Bayesian formulation of the planted partition model. We show that this approach succeeds in finding statistically significant assortative modules in networks, unlike alternatives such as modularity maximization, which systematically overfits both in artificial as well as in empirical examples. In addition, we show that our method is not subject to a resolution limit, and can uncover an arbitrarily large number of communities, as long as there is statistical evidence for them. Our formulation is amenable to model selection procedures, which allow us to compare it to more general approaches based on the stochastic block model, and in this way reveal whether assortativity is in fact the dominating large-scale mixing pattern. We perform this comparison with several empirical networks, and identify numerous cases where the network's assortativity is exaggerated by traditional community detection methods, and we show how a more faithful degree of assortativity can be identified.