ITP OpenIR  > 计算平台成果
Shi, C1; Liu, YC; Zhang, P
Weighted community detection and data clustering using message passing
Source PublicationJOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
Language英语
KeywordNETWORKS MODEL
AbstractGrouping objects into clusters based on the similarities or weights between them is one of the most important problems in science and engineering. In this work, by extending message-passing algorithms and spectral algorithms proposed for an unweighted community detection problem, we develop a nonparametric method based on statistical physics, by mapping the problem to the Potts model at the critical temperature of spin-glass transition and applying belief propagation to solve the marginals corresponding to the Boltzmann distribution. Our algorithm is robust to over-fitting and gives a principled way to determine whether there are significant clusters in the data and how many clusters there are. We apply our method to different clustering tasks. In the community detection problem in weighted and directed networks, we show that our algorithm significantly outperforms existing algorithms. In the clustering problem, where the data were generated by mixture models in the sparse regime, we show that our method works all the way down to the theoretical limit of detectability and gives accuracy very close to that of the optimal Bayesian inference. In the semi-supervised clustering problem, our method only needs several labels to work perfectly in classic datasets. Finally, we further develop Thouless-Anderson-Palmer equations which heavily reduce the computation complexity in dense networks but give almost the same performance as belief propagation.
2018
ISSN1742-5468
Pages33405
Cooperation Status国际
Subject AreaMechanics ; Physics
MOST Discipline CatalogueMechanics ; Physics, Mathematical
DOI10.1088/1742-5468/aaa8f5
Indexed BySCI
Funding OrganizationChinese Academy of Sciences ; Chinese Academy of Sciences
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.itp.ac.cn/handle/311006/22740
Collection计算平台成果
Affiliation1.Chinese Acad Sci, Inst Theoret Phys, CAS Key Lab Theoret Phys, Beijing 100190, Peoples R China
2.Univ Elect Sci & Technol China, CompleX Lab, Web Sci Ctr, Chengdu 611731, Sichuan, Peoples R China
3.Northeastern Univ, Network Sci Inst, 177 Huntington Ave, Boston, MA 02115 USA
Recommended Citation
GB/T 7714
Shi, C,Liu, YC,Zhang, P. Weighted community detection and data clustering using message passing[J]. JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT,2018:33405.
APA Shi, C,Liu, YC,&Zhang, P.(2018).Weighted community detection and data clustering using message passing.JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT,33405.
MLA Shi, C,et al."Weighted community detection and data clustering using message passing".JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT (2018):33405.
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