Node attributes are ubiquitous in real-world networks, whose fusion with graph topology raises new challenges for the detection and understanding of community structure. Due to their principled characterization and interpretation , probabilistic generative models (PGMs) have become the mainstream methods for community detection in attributed networks. Most existing PGMs require the attributes to be categorical or Gaussian distributed. A novel PGM is proposed here to overcome these limitations. For the generality of our model, we consider the impact of the distances between attributes on node popularity , whose description raises the model selection problem in statistical inference. We present a novel scheme to address this issue by analyzing the detectability of communities for our model , which is also a quantitative description on the effect of node attributes with respect to communities . With the model determined, an efficient algorithm is then developed to estimate the parameters and  infer the communities. Extensive experiments have been conducted to validate our work from two aspects. First, the experiments on artificial networks verified the detectability condition for our model . Second, the comparison on various real-world datasets shows that our algorithm outperforms the competitive methods .
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 .
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whose fusion with graph topology raises new challenges for the detection and understanding of community structure. Due to their principled characterization and interpretation
communities can be reflected by abundant node attributes with the fusion of graph topology. In attribute-aware community detection
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methods for community detection in attributed networks. Most existing PGMs require the
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
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A novel PGM is proposed here to overcome these limitations. For the generality of our model, we consider the impact of the distances between attributes
Based on the famous block model of graph structure, our model fuses the attribute by describing its effect
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, whose description raises the model selection problem in statistical inference. We present a novel scheme to address this issue by analyzing the
using an additional term. To characterize the effect quantitatively, we analyze the
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Extensive experiments have been conducted to validate our work
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the comparison on various
extensive experiments show that our method outperforms the competing approaches on a variety of
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