Community Detection in Multi-Layer Networks Using Joint Nonnegative Matrix Factorization
文献类型：期刊论文
| 作者 | Ma, Xiaoke1; Dong, Di2 ; Wang, Quan1
|
| 刊名 | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
 |
| 出版日期 | 2019-02-01 |
| 卷号 | 31期号:2页码:273-286 |
| 关键词 | Multi-layer networks community structure nonnegative matrix factorization semi-supervised clustering |
| ISSN号 | 1041-4347 |
| DOI | 10.1109/TKDE.2018.2832205 |
| 通讯作者 | Ma, Xiaoke(xkma@xidian.edu.cn) |
| 英文摘要 | Many 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. |
| WOS关键词 | PREDICTION ; ALGORITHMS ; EXPRESSION ; CUTS |
| 资助项目 | 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] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000456148800006 |
| 出版者 | IEEE COMPUTER SOC |
| 资助机构 | NSFC ; International Cooperation and Exchange of the NSFC ; Science & Technology Program of Shannxi Province ; Fundamental Research Funding of Central Universities |
| 源URL | [http://ir.ia.ac.cn/handle/173211/25343]  |
| 专题 | 自动化研究所_中国科学院分子影像重点实验室 |
| 通讯作者 | Ma, Xiaoke |
| 作者单位 | 1.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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：自动化研究所
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