The overlapping community driven feedback mechanism to support consensus in social network group decision making

Abstract—Social network group decision-making (SN-GDM) provides valuable support for obtaining agreed decision results by effectively utilizing the connected social trust relationships among individuals. However, the impact of intricately overlapped social trust relationships within overlapping communities on evaluation modifications in the SN-GDM consensus reaching process is seldom considered. To alleviate this issue, this study attempts to construct an overlapping community driven feedback mechanism for improving consensus in SN-GDM. The Lancichinetti-Fortunato method (LFM) is used to detect the overlapping community structures under social trust networks. Subsequently, the trusted recommendation advice is conducted within overlapping communities, which guides the inconsistent subgroups to make an interaction with each other to reach higher consensus level. Then, an associated feedback mechanism for SNGDM with overlapping communities is proposed, which enables the inconsistent subgroups to minimize the consensus cost by selecting personalized feedback parameters. Moreover, it shows that the overlapping communities based feedback mechanism is superior to the feedback mechanisms with non-overlapping communities. Finally, an illustrative example is included, which is also used to testify the efficacy of proposal by comparing the consensus cost under different representative recommendation advice in overlapping social trust networks.