Nonlinear Structural Fusion for Multiplex Network
Many real-world complex systems have multiple types of relations between their components, and they are popularly modeled as multiplex networks with each type of relation as one layer. Since the fusion analysis of multiplex networks can provide a comprehensive insight, the structural information fus...
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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/10.1155/2020/7041564 |
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| _version_ | 1832566005473214464 |
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| author | Nianwen Ning Feiyu Long Chunchun Wang Youjie Zhang Yilin Yang Bin Wu |
| author_facet | Nianwen Ning Feiyu Long Chunchun Wang Youjie Zhang Yilin Yang Bin Wu |
| author_sort | Nianwen Ning |
| collection | DOAJ |
| description | Many real-world complex systems have multiple types of relations between their components, and they are popularly modeled as multiplex networks with each type of relation as one layer. Since the fusion analysis of multiplex networks can provide a comprehensive insight, the structural information fusion of multiplex networks has become a crucial issue. However, most of these existing data fusion methods are inappropriate for researchers to apply to complex network analysis directly. The feature-based fusion methods ignore the sharing and complementarity of interlayer structural information. To tackle this problem, we propose a multiplex network structural fusion (MNSF) model, which can construct a network with comprehensive information. It is composed of two modules: the network feature extraction (NFE) module and the network structural fusion (NSF) module. (1) In NFE, MNSF first extracts a low-dimensional vector representation of a node from each layer. Then, we construct a node similarity network based on embedding matrices and K-D tree algorithm. (2) In NSF, we present a nonlinear enhanced iterative fusion (EIF) strategy. EIF can strengthen high-weight edges presented in one (i.e., complementary information) or more (i.e., shared information) networks and weaken low-weight edges (i.e., redundant information). The retention of low-weight edges shared by all layers depends on the tightness of connections of their K-order proximity. The usage of higher-order proximity in EIF alleviates the dependence on the quality of node embedding. Besides, the fused network can be easily exploited by traditional single-layer network analysis methods. Experiments on real-world networks demonstrate that MNSF outperforms the state-of-the-art methods in tasks link prediction and shared community detection. |
| format | Article |
| id | doaj-art-8260b7cbd4c648aa878ef9a223129cfa |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-8260b7cbd4c648aa878ef9a223129cfa2025-02-03T01:05:22ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/70415647041564Nonlinear Structural Fusion for Multiplex NetworkNianwen Ning0Feiyu Long1Chunchun Wang2Youjie Zhang3Yilin Yang4Bin Wu5Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, No. 10, Xitucheng Road, Beijing 100876, ChinaBeijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, No. 10, Xitucheng Road, Beijing 100876, ChinaChina United Network Communication Co., Ltd., No. 222, Zhongzhou Middle Road, Luoyang 471000, ChinaBeijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, No. 10, Xitucheng Road, Beijing 100876, ChinaBeijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, No. 10, Xitucheng Road, Beijing 100876, ChinaBeijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, No. 10, Xitucheng Road, Beijing 100876, ChinaMany real-world complex systems have multiple types of relations between their components, and they are popularly modeled as multiplex networks with each type of relation as one layer. Since the fusion analysis of multiplex networks can provide a comprehensive insight, the structural information fusion of multiplex networks has become a crucial issue. However, most of these existing data fusion methods are inappropriate for researchers to apply to complex network analysis directly. The feature-based fusion methods ignore the sharing and complementarity of interlayer structural information. To tackle this problem, we propose a multiplex network structural fusion (MNSF) model, which can construct a network with comprehensive information. It is composed of two modules: the network feature extraction (NFE) module and the network structural fusion (NSF) module. (1) In NFE, MNSF first extracts a low-dimensional vector representation of a node from each layer. Then, we construct a node similarity network based on embedding matrices and K-D tree algorithm. (2) In NSF, we present a nonlinear enhanced iterative fusion (EIF) strategy. EIF can strengthen high-weight edges presented in one (i.e., complementary information) or more (i.e., shared information) networks and weaken low-weight edges (i.e., redundant information). The retention of low-weight edges shared by all layers depends on the tightness of connections of their K-order proximity. The usage of higher-order proximity in EIF alleviates the dependence on the quality of node embedding. Besides, the fused network can be easily exploited by traditional single-layer network analysis methods. Experiments on real-world networks demonstrate that MNSF outperforms the state-of-the-art methods in tasks link prediction and shared community detection.http://dx.doi.org/10.1155/2020/7041564 |
| spellingShingle | Nianwen Ning Feiyu Long Chunchun Wang Youjie Zhang Yilin Yang Bin Wu Nonlinear Structural Fusion for Multiplex Network Complexity |
| title | Nonlinear Structural Fusion for Multiplex Network |
| title_full | Nonlinear Structural Fusion for Multiplex Network |
| title_fullStr | Nonlinear Structural Fusion for Multiplex Network |
| title_full_unstemmed | Nonlinear Structural Fusion for Multiplex Network |
| title_short | Nonlinear Structural Fusion for Multiplex Network |
| title_sort | nonlinear structural fusion for multiplex network |
| url | http://dx.doi.org/10.1155/2020/7041564 |
| work_keys_str_mv | AT nianwenning nonlinearstructuralfusionformultiplexnetwork AT feiyulong nonlinearstructuralfusionformultiplexnetwork AT chunchunwang nonlinearstructuralfusionformultiplexnetwork AT youjiezhang nonlinearstructuralfusionformultiplexnetwork AT yilinyang nonlinearstructuralfusionformultiplexnetwork AT binwu nonlinearstructuralfusionformultiplexnetwork |