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Community Detection in Multi-Layer Networks Using Joint Nonnegative Matrix Factorization
Ma, Xiaoke1; Dong, Di2; Wang, Quan1
Source PublicationIEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN1041-4347
2019-02-01
Volume31Issue:2Pages:273-286
Corresponding AuthorMa, Xiaoke(xkma@xidian.edu.cn)
AbstractMany complex systems are composed of coupled networks through different layers, where each layer represents one of many possible types of interactions. A fundamental question is how to extract communities in multi-layer networks. The current algorithms either collapses multi-layer networks into a single-layer network or extends the algorithms for single-layer networks by using consensus clustering. However, these approaches have been criticized for ignoring the connection among various layers, thereby resulting in low accuracy. To attack this problem, a quantitative function (multi-layer modularity density) is proposed for community detection in multi-layer networks. Afterward, we prove that the trace optimization of multi-layer modularity density is equivalent to the objective functions of algorithms, such as kernel K-means, nonnegative matrix factorization (NMF), spectral clustering and multi-view clustering, for multi-layer networks, which serves as the theoretical foundation for designing algorithms for community detection. Furthermore, a Semi-Supervised joint Nonnegative Matrix Factorization algorithm (S2-jNMF) is developed by simultaneously factorizing matrices that are associated with multi-layer networks. Unlike the traditional semi-supervised algorithms, the partial supervision is integrated into the objective of the S2-jNMF algorithm. Finally, through extensive experiments on both artificial and real world networks, we demonstrate that the proposed method outperforms the state-of-the-art approaches for community detection in multi-layer networks.
KeywordMulti-layer networks community structure nonnegative matrix factorization semi-supervised clustering
DOI10.1109/TKDE.2018.2832205
WOS KeywordPREDICTION ; ALGORITHMS ; EXPRESSION ; CUTS
Indexed BySCI
Language英语
Funding ProjectNSFC[61772394] ; NSFC[61502363] ; NSFC[61572385] ; International Cooperation and Exchange of the NSFC[61711530248] ; Science & Technology Program of Shannxi Province[2015KTCXSF-01] ; Fundamental Research Funding of Central Universities[JB180304] ; NSFC[61772394] ; NSFC[61502363] ; NSFC[61572385] ; International Cooperation and Exchange of the NSFC[61711530248] ; Science & Technology Program of Shannxi Province[2015KTCXSF-01] ; Fundamental Research Funding of Central Universities[JB180304]
Funding OrganizationNSFC ; International Cooperation and Exchange of the NSFC ; Science & Technology Program of Shannxi Province ; Fundamental Research Funding of Central Universities
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS IDWOS:000456148800006
PublisherIEEE COMPUTER SOC
Citation statistics
Cited Times:31[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25344
Collection中国科学院分子影像重点实验室
Corresponding AuthorMa, Xiaoke
Affiliation1.Xidian Univ, Sch Comp Sci & Technol, Xian 710071, Shaanxi, Peoples R China
2.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Ma, Xiaoke,Dong, Di,Wang, Quan. Community Detection in Multi-Layer Networks Using Joint Nonnegative Matrix Factorization[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2019,31(2):273-286.
APA Ma, Xiaoke,Dong, Di,&Wang, Quan.(2019).Community Detection in Multi-Layer Networks Using Joint Nonnegative Matrix Factorization.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,31(2),273-286.
MLA Ma, Xiaoke,et al."Community Detection in Multi-Layer Networks Using Joint Nonnegative Matrix Factorization".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 31.2(2019):273-286.
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