期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 52, 期 12, 页码 12854-12868出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2021.3090769
关键词
Nonhomogeneous media; Task analysis; Multitasking; Optimization; Knowledge engineering; Couplings; Correlation; Evolutionary multitasking optimization (EMTO); knowledge transfer; multilayer network; network reconstruction
类别
资金
- Key Project of Science and Technology Innovation 2030
- Ministry of Science and Technology of China [2018AAA0101302]
- General Program of National Natural Science Foundation of China (NSFC) [61773300]
- Fundamental Research Funds for the Central Universities [XJS211905]
This article introduces an evolutionary multitasking multilayer network reconstruction framework EM2MNR to enhance reconstruction performance by utilizing correlations among different component layers. By utilizing restricted Boltzmann machine to extract low effective features and deciding on knowledge transfer, the proposed framework significantly improves reconstruction performance on multilayer network reconstruction problems.
Due to the multilayer nature of real-world systems, the problem of inferring multilayer network structures from nonlinear and complex dynamical systems is prominent in many fields, including engineering, biological, physical, and computer sciences. Many network reconstruction methods have been proposed to address this problem, but none of them consider the similarities among network reconstruction tasks at different component layers, which are inspired by topology correlations and dynamic couplings among different component layers. This article develops an evolutionary multitasking multilayer network reconstruction framework to make use of the correlations among different component layers to improve the reconstruction performance; we refer to this framework as EM2MNR. In EM2MNR, the multilayer network reconstruction problem is first established as a multitasking multilayer network reconstruction problem, where the goal of each task is to reconstruct the network structure of a component layer. In addition, multitasking multilayer network reconstruction problems are high dimensional, but existing evolutionary multitasking algorithms may have poor performance when dealing with optimization problems with a high-dimensional search space. Inspired by the sparsity of multilayer networks, EM2MNR employs the restricted Boltzmann machine to extract low effective features from the original decision space and then decides whether to conduct knowledge transfer on these features. To verify the performance of EM2MNR, this article also designs a test suite for multilayer network reconstruction problems. The experimental results demonstrate the significant improvement obtained by the proposed EM2MNR framework on 96 multilayer network reconstruction problems.
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