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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\u2013Maximization\u2013Aggregation (EMA) framework to reconstruct the complete structure of multiplex networks. We validate the proposed EMA framework against the Expectation\u2013Maximization (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.<\/jats:p>","DOI":"10.3390\/e25010142","type":"journal-article","created":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T02:06:03Z","timestamp":1673402763000},"page":"142","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Reconstructing Sparse Multiplex Networks with Application to Covert Networks"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0973-7725","authenticated-orcid":false,"given":"Jin-Zhu","family":"Yu","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, University of Texas at Arlington, Arlington, TX 76019, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mincheng","family":"Wu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8686-9353","authenticated-orcid":false,"given":"Gisela","family":"Bichler","sequence":"additional","affiliation":[{"name":"School of Criminology and Criminal Justice, California State University, San Bernardino, CA 92407, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Felipe","family":"Aros-Vera","sequence":"additional","affiliation":[{"name":"Department of Industrial and Systems Engineering, Ohio University, Athens, OH 45701, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3952-208X","authenticated-orcid":false,"given":"Jianxi","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Rensselaer Polytechnic Institute (RPI), Troy, NY 12180, USA"},{"name":"Network Science and Technology Center, Rensselaer Polytechnic Institute (RPI), Troy, NY 12180, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gao, J., Bashan, A., Shekhtman, L., and Havlin, S. 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