Reconstructing Sparse Multiplex Networks with Application to Covert Networks
Network structure provides critical information for understanding the dynamic behavior of complex systems. However, the complete structure of real-world networks is often unavailable, thus it is crucially important to develop approaches to infer a more complete structure of networks. In this paper,...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/25/1/142 |
| _version_ | 1797442932436893696 |
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| author | Jin-Zhu Yu Mincheng Wu Gisela Bichler Felipe Aros-Vera Jianxi Gao |
| author_facet | Jin-Zhu Yu Mincheng Wu Gisela Bichler Felipe Aros-Vera Jianxi Gao |
| author_sort | Jin-Zhu Yu |
| collection | DOAJ |
| description | Network structure provides critical information for understanding the dynamic behavior of complex systems. However, the complete structure of real-world networks is often unavailable, thus it is crucially important to develop approaches to infer a more complete structure of networks. In this paper, we integrate the configuration model for generating random networks into an Expectation–Maximization–Aggregation (EMA) framework to reconstruct the complete structure of multiplex networks. We validate the proposed EMA framework against the Expectation–Maximization (EM) framework and random model on several real-world multiplex networks, including both covert and overt ones. It is found that the EMA framework generally achieves the best predictive accuracy compared to the EM framework and the random model. As the number of layers increases, the performance improvement of EMA over EM decreases. The inferred multiplex networks can be leveraged to inform the decision-making on monitoring covert networks as well as allocating limited resources for collecting additional information to improve reconstruction accuracy. For law enforcement agencies, the inferred complete network structure can be used to develop more effective strategies for covert network interdiction. |
| first_indexed | 2024-03-09T12:48:47Z |
| format | Article |
| id | doaj.art-31599702920a45d68fb4b665143b596f |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T12:48:47Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-31599702920a45d68fb4b665143b596f2023-11-30T22:09:17ZengMDPI AGEntropy1099-43002023-01-0125114210.3390/e25010142Reconstructing Sparse Multiplex Networks with Application to Covert NetworksJin-Zhu Yu0Mincheng Wu1Gisela Bichler2Felipe Aros-Vera3Jianxi Gao4Department of Civil Engineering, University of Texas at Arlington, Arlington, TX 76019, USAState Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310058, ChinaSchool of Criminology and Criminal Justice, California State University, San Bernardino, CA 92407, USADepartment of Industrial and Systems Engineering, Ohio University, Athens, OH 45701, USADepartment of Computer Science, Rensselaer Polytechnic Institute (RPI), Troy, NY 12180, USANetwork structure provides critical information for understanding the dynamic behavior of complex systems. However, the complete structure of real-world networks is often unavailable, thus it is crucially important to develop approaches to infer a more complete structure of networks. In this paper, we integrate the configuration model for generating random networks into an Expectation–Maximization–Aggregation (EMA) framework to reconstruct the complete structure of multiplex networks. We validate the proposed EMA framework against the Expectation–Maximization (EM) framework and random model on several real-world multiplex networks, including both covert and overt ones. It is found that the EMA framework generally achieves the best predictive accuracy compared to the EM framework and the random model. As the number of layers increases, the performance improvement of EMA over EM decreases. The inferred multiplex networks can be leveraged to inform the decision-making on monitoring covert networks as well as allocating limited resources for collecting additional information to improve reconstruction accuracy. For law enforcement agencies, the inferred complete network structure can be used to develop more effective strategies for covert network interdiction.https://www.mdpi.com/1099-4300/25/1/142multiplex networkspartially observable networksinterlayer dependencynetwork completionexpectation–maximization |
| spellingShingle | Jin-Zhu Yu Mincheng Wu Gisela Bichler Felipe Aros-Vera Jianxi Gao Reconstructing Sparse Multiplex Networks with Application to Covert Networks Entropy multiplex networks partially observable networks interlayer dependency network completion expectation–maximization |
| title | Reconstructing Sparse Multiplex Networks with Application to Covert Networks |
| title_full | Reconstructing Sparse Multiplex Networks with Application to Covert Networks |
| title_fullStr | Reconstructing Sparse Multiplex Networks with Application to Covert Networks |
| title_full_unstemmed | Reconstructing Sparse Multiplex Networks with Application to Covert Networks |
| title_short | Reconstructing Sparse Multiplex Networks with Application to Covert Networks |
| title_sort | reconstructing sparse multiplex networks with application to covert networks |
| topic | multiplex networks partially observable networks interlayer dependency network completion expectation–maximization |
| url | https://www.mdpi.com/1099-4300/25/1/142 |
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