2 research outputs found
Statistical inference of assortative community structures
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.Comment: 15 pages, 6 figures. Code is available at
https://graph-tool.skewed.de and a HOWTO documentation at
https://graph-tool.skewed.de/static/doc/demos/inference/inference.htm