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An interpretable evaluation framework for complex systems integrating network science and data analysis
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AI
DOI：10.1016/j.eswa.2026.131317.png)
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Abstract
En 中文
Complex systems are increasingly prevalent across a wide range of sectors, including healthcare, industrial production, and financial risk management. However, existing evaluation methods often struggle to provide objective and interpretable evaluations when dealing with high-dimensional, diverse, and nonlinear systems. This paper proposes a novel Network Comprehensive Evaluation (NCE) method that integrates statistical modeling techniques with complex network theory, offering a new framework for the comprehensive evaluation of complex systems. The NCE method is grounded in well-established mathematical and information technologies, ensuring both theoretical rigor and interpretability throughout the evaluation process. Unlike traditional approaches that rely on manually defined evaluation indicators and systems, or emerging methods such as machine learning and deep learning that often lack integration with domain-specific knowledge, the NCE method enhances both the reliability and applicability of evaluation results. The method is demonstrated through case studies in diverse fields, including the diagnosis of hepatitis, the performance evaluation of aero-engines, and the risk evaluation of financial systems, illustrating its effectiveness and broad applicability across different complex systems.
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