Consensus reaching in LSGDM: Overlapping community detection and bounded confidence-driven feedback mechanism

Abstract

The surge of social media has made large-scale group decision-making (LSGDM) crucial in real-world decision-making. The intricacies of trust relationships within social networks that emerged from relations in social media affect both the clustering and the consensus of large groups. However, existing research often neglects the impact of overlapping social trust networks on group consensus. To fill this gap, this study introduces a novel consensus-reaching process (CRP) that integrates overlapping community detection and ELICIT-based optimization models under bounded confidence. Initially, the Lancichinetti-Fortunato method (LFM) is employed to identify overlapping community structures within social trust networks, delineating several subgroups and identifying corresponding non-overlapping and overlapping decision-makers (DMs). Subsequently, the PageRank (PR) algorithm is utilized to compute both global and local weights for individuals, facilitating a rational aggregation of collective and subgroup opinions. Next, two-stage Extended Comparative Linguistic Expressions With Symbolic Translation (ELICIT)-based optimization consensus models under bounded confidence are designed, aiming to provide optimal feedback for guiding DMs' preference adjustments. Since overlapping DMs may belong to multiple subgroups, a weighted influence feedback mechanism is introduced to mitigate conflicting guidance from these multiple affiliations. Finally, we demonstrate the effectiveness and superiority of our proposed method through numerical validation and comparative analysis against existing approaches.