As a fundamental structure in real-world networks, communities can be
reflected by abundant node attributes with the fusion of graph topology. In
attribute-aware community detection, probabilistic generative models (PGMs)
have become the mainstream fusion method due to their principled
characterization and interpretation. Here, we propose a novel PGM without
imposing any distributional assumptions on attributes, which is superior to
existing PGMs that require attributes to be categorical or Gaussian
distributed. Based on the famous block model of graph structure, our model
fuses the attribute by describing its effect on node popularity using an
additional term. To characterize the effect quantitatively, we analyze the
detectability of communities for the proposed model and then establish the
requirements of the attribute-popularity term, which leads to a new scheme for
the model selection problem in attribute-aware community detection. 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, whose correctness is verified by
numerical experiments on artificial graphs. Second, extensive experiments show
that our method outperforms the competing approaches on a variety of real-world
networks.Comment: other authors do not want to preprin