A Unified Bayesian Model for Generalized Community Detection in Attribute Networks
Identification of community structures and the underlying semantic characteristics of communities are essential tasks in complex network analysis. However, most methods proposed so far are typically only applicable to assortative community structures, that is, more links within communities and fewer...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/5712815 |
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| _version_ | 1832554079580061696 |
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| author | Qiang Tian Wenjun Wang Yingjie Xie Huaming Wu Pengfei Jiao Lin Pan |
| author_facet | Qiang Tian Wenjun Wang Yingjie Xie Huaming Wu Pengfei Jiao Lin Pan |
| author_sort | Qiang Tian |
| collection | DOAJ |
| description | Identification of community structures and the underlying semantic characteristics of communities are essential tasks in complex network analysis. However, most methods proposed so far are typically only applicable to assortative community structures, that is, more links within communities and fewer links between different communities, which ignore the rich diversity of community regularities in real networks. In addition, the node attributes that provide rich semantics information of communities and networks can facilitate in-depth community detection of structural information. In this paper, we propose a novel unified Bayesian generative model to detect generalized communities and provide semantic descriptions simultaneously by combining network topology and node attributes. The proposed model is composed of two closely correlated parts by a transition matrix; we first apply the concept of a mixture model to describe network regularities and then adjust the classic Latent Dirichlet Allocation (LDA) topic model to identify community semantically. Thus, the model can detect broad types of network structure regularities, including assortative structures, disassortative structures, and mixture structures and provide multiple semantic descriptions for the communities. To optimize the objective function of the model, we use an effective Gibbs sampling algorithm. Experiments on a number of synthetic and real networks show that our model has superior performance compared with some baselines on community detection. |
| format | Article |
| id | doaj-art-47d6b8b51469417fb0bab049d4bce928 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-47d6b8b51469417fb0bab049d4bce9282025-02-03T05:52:29ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/57128155712815A Unified Bayesian Model for Generalized Community Detection in Attribute NetworksQiang Tian0Wenjun Wang1Yingjie Xie2Huaming Wu3Pengfei Jiao4Lin Pan5College of Intelligence and Computing, Tianjin University, Tianjin, ChinaCollege of Intelligence and Computing, Tianjin University, Tianjin, ChinaCollege of Intelligence and Computing, Tianjin University, Tianjin, ChinaCenter of Applied Mathematics, Tianjin University, Tianjin, ChinaCenter of Biosafety Research and Strategy, Law School, Tianjin University, Tianjin, ChinaSchool of Marine Science and Technology, Tianjin University, Tianjin, ChinaIdentification of community structures and the underlying semantic characteristics of communities are essential tasks in complex network analysis. However, most methods proposed so far are typically only applicable to assortative community structures, that is, more links within communities and fewer links between different communities, which ignore the rich diversity of community regularities in real networks. In addition, the node attributes that provide rich semantics information of communities and networks can facilitate in-depth community detection of structural information. In this paper, we propose a novel unified Bayesian generative model to detect generalized communities and provide semantic descriptions simultaneously by combining network topology and node attributes. The proposed model is composed of two closely correlated parts by a transition matrix; we first apply the concept of a mixture model to describe network regularities and then adjust the classic Latent Dirichlet Allocation (LDA) topic model to identify community semantically. Thus, the model can detect broad types of network structure regularities, including assortative structures, disassortative structures, and mixture structures and provide multiple semantic descriptions for the communities. To optimize the objective function of the model, we use an effective Gibbs sampling algorithm. Experiments on a number of synthetic and real networks show that our model has superior performance compared with some baselines on community detection.http://dx.doi.org/10.1155/2020/5712815 |
| spellingShingle | Qiang Tian Wenjun Wang Yingjie Xie Huaming Wu Pengfei Jiao Lin Pan A Unified Bayesian Model for Generalized Community Detection in Attribute Networks Complexity |
| title | A Unified Bayesian Model for Generalized Community Detection in Attribute Networks |
| title_full | A Unified Bayesian Model for Generalized Community Detection in Attribute Networks |
| title_fullStr | A Unified Bayesian Model for Generalized Community Detection in Attribute Networks |
| title_full_unstemmed | A Unified Bayesian Model for Generalized Community Detection in Attribute Networks |
| title_short | A Unified Bayesian Model for Generalized Community Detection in Attribute Networks |
| title_sort | unified bayesian model for generalized community detection in attribute networks |
| url | http://dx.doi.org/10.1155/2020/5712815 |
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