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 an appreciable 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.

中文翻译：

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分类社区结构的统计推断

我们开发了一种原则上的方法来推断基于种植分区模型的非参数贝叶斯公式的网络中的分类社区。我们表明，这种方法成功地在网络中找到了具有统计意义的分类模块，这与诸如模块化最大化等替代方案不同，后者在人工和经验示例中都系统地过度拟合。另外，我们表明，只要有统计依据，我们的方法就不会受到明显的分辨率限制，并且可以发现任意数量的社区。我们的公式适合模型选择程序，这使我们可以将其与基于随机块模型的更通用方法进行比较，并以此方式揭示出分类性是否实际上是主要的大规模混合模式。