Consensus-reaching strategy is crucial in large-scale group decision-making (LSGDM) as it serves as an effective approach to reducing group conflicts. Meanwhile, the common social network relationships in large groups can affect information exchange, thereby influencing the consensus-reaching process (CRP) and decision results. Therefore, how to leverage social network information in LSGDM to obtain an agreed solution has received widespread attention. However, most existing research assumes relative independence between communities in the dimension reduction process of LSGDM and neglects the possibility of different overlaps between them. Moreover, the impact of overlapping communities on CRP has not been adequately explored. Besides, the dynamic variations in clusters and their weights caused by evaluation updates need to be further studied. To address these issues, this paper proposes a dynamic clustering-based consensus-reaching method for LSGDM considering the impact of overlapping communities. First, the LINE-based label propagation algorithm is designed to cluster decision makers (DMs) and detect overlapping communities with social network information. An overlapping community-driven feedback mechanism is then developed to enhance group consensus by utilizing the bridging role of overlapping DMs. During CRP, clusters and their weights are dynamically updated with trust evolution due to the evaluation iteration. Finally, a case study using the Film Trust dataset is conducted to verify the effectiveness of the proposed method. Simulation experiments and comparative analysis demonstrate the capability of our method in modeling practical scenarios and addressing LSGDM problems under social network contexts.

中文翻译：

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基于动态聚类的共识模型，用于考虑重叠社区的大规模群体决策


达成共识策略在大规模群体决策 （LSGDM） 中至关重要，因为它是减少群体冲突的有效方法。同时，大型群体中常见的社交网络关系会影响信息交换，从而影响共识达成过程 （CRP） 和决策结果。因此，如何利用 LSGDM 中的社交网络信息来获得一致的解决方案受到了广泛的关注。然而，大多数现有研究假设在 LSGDM 的降维过程中群落之间相对独立，而忽略了它们之间不同重叠的可能性。此外，重叠群落对 CRP 的影响尚未得到充分探讨。此外，评估更新引起的聚类及其权重的动态变化需要进一步研究。为了解决这些问题，本文提出了一种基于动态聚类的 LSGDM 共识达成方法，考虑了重叠社区的影响。首先，基于 LINE 的标签传播算法旨在对决策者 （DM） 进行聚类，并检测与社交网络信息重叠的社区。然后开发一个重叠的社区驱动的反馈机制，通过利用重叠 DM 的桥接作用来提高群体共识。在 CRP 期间，由于评估迭代，集群及其权重会随着信任演变而动态更新。最后，使用 Film Trust 数据集进行了案例研究，以验证所提方法的有效性。仿真实验和比较分析证明了我们的方法在对实际场景进行建模和解决社交网络背景下的 LSGDM 问题方面的能力。