  As a fundamental structure in real-world networks, in addition to graph
topology, communities can also be reflected by abundant node attributes. In
attributed community detection, probabilistic generative models (PGMs) have
become the mainstream method due to their principled characterization and
competitive performances. Here, we propose a novel PGM without imposing any
distributional assumptions on attributes, which is superior to the existing
PGMs that require attributes to be categorical or Gaussian distributed. Based
on the block model of graph structure, our model incorporates the attribute by
describing its effect on node popularity. To characterize the effect
quantitatively, we analyze the community detectability for our model and then
establish the requirements of the node popularity term. This leads to a new
scheme for the crucial model selection problem in choosing and solving
attributed community detection models. With the model determined, an efficient
algorithm is developed to estimate the parameters and to infer the communities.
The proposed method is validated from two aspects. First, the effectiveness of
our algorithm is theoretically guaranteed by the detectability condition.
Second, extensive experiments indicate that our method not only outperforms the
competing approaches on the employed datasets, but also shows better
applicability to networks with various node attributes.
