A Novel Two-Phase Social Network-Based Consensus Mechanism: Opinion Dynamics for Large-Scale Group Decision-Making

The emergence of large-scale group decision-making (LSGDM) has generated significant interest and pre-sented novel issues for decision-makers. This article presents a framework consisting of a two-phase technique that assists decision-makers in large-scale groups in achieving consensus by integrating a social network context and a consensus-reaching approach. Initially, the analysis focuses on the social network of a significant number of decision-makers to examine the impact of social relationships. This network is then utilized to allocate weights to the decision-makers. The suggestion counsel can be developed using social relationships to aggregate group viewpoints into a collective resource. Furthermore, the clustering approach of K-Means, based on Eigen Vector centrality, has been employed to discover distinct subgroups that do not overlap. Additionally, a modified multi-voting technique has been used to facilitate achieving a consensus. We conducted a comparative analysis with other current methodologies, and our results demonstrate superior production compared to the others.