Adaptive opinion evolution process with opinion dynamics for large-scale group decision making: A novel approach based on overlapping community detection in social networks

The rapid development of Web2.0 technologies and social media platforms has developed a new paradigm that allows many individuals to participate in decision-making processes within online social networks, leading to the rise of social network group decision making (SNGDM). Existing research in SNGDM primarily focus on small-scale DMs, which may not be suitable for large-scale SNGDM problems due to the high costs and time constraints of adjustments. Moreover, the non-overlapping community structure in social network encounter several limitations, further complicating the resolution of large-scale SNGDM problems. In this study, we propose an adaptive opinion evolution process with opinion dynamics for large-scale SNGDM. The proposed approach comprises four stages: classification of decision makers (DMs), determination of community weights, consensus reaching process, and alternative selection. A real-world application is utilized to demonstrate the effectiveness of the proposed method, and a comparison with existing related works highlights the advantages and innovation of the proposed model.