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Abstract

This paper proposes a novel large-scale group decision-making (LSGDM) framework based on complex network theory. First, by enhancing the clustering efficiency of the Louvain algorithm through complex network theory, this framework enables community classification of a large-scale expert group, thereby reduce the complexity of the expert network and improve decision-making efficiency. Second, a novel communicability-driven expert influence identification model within expert social networks is designed, and a new consensus feedback mechanism is proposed. This approach comprehensively considers both the individual influence of experts and the impact of their interactions to guide consensus-reaching, which can accurately reflect the actual flow of information within the network. Third, by extending the Best–Worst Method and Shannon Entropy Method to the context of fuzzy information, a balance between subjectivity and objectivity is obtained, which can make more accurate and interpretable decision results. The proposed method is validated through a case study on green material selection. Moreover, its superiority is further demonstrated through comparative experiments. The results show that our method demonstrates a 9.89% reduction in opinion revision costs with accelerated consensus-reaching efficiency.