Game-theoretic multi-granularity consensus adjustment for social network group decision-making

Abstract

The continuous trend development of information intelligence has made social network group decision-making (SN-GDM) increasingly important. However, in the context of SN-GDM, there are three key challenges: how to properly handle the decline in the trust propagation efficiency; the single granularity consensus adjustment strategy may not fully consider the impact of groups with high consensus on costs and the different cooperation intention of subgroups; traditional methods often face challenges in determining high-performance classification thresholds. In response to the above issues, the solution involves reconstructing trust relationships and optimizing clustering via the Leiden algorithm (LA) and structural holes (SHs). Moreover, multi-granularity consensus adjustment is implemented using game theory while classification thresholds are refined with game-theoretic rough sets (GTRSs). More specifically, firstly, the discount rate of trust propagation intermediaries and path reliability are considered to conduct indirect trust propagation and multipath fusion, and the cooperation index (CI) is obtained based on trust relationships and similarities. Secondly, LA is used to cluster DMs, with the CI as the edge weight, to ensure that the community reflects social relationships and opinion consensus. The weight of decision-makers (DMs) and subgroups are objectively determined by comprehensively integrating SHs, CI, similarity and in-degree centrality. Thirdly, a multi-granularity consensus adjustment method involving game theory is proposed. This method considers three adjustment scenarios: joint adjustment, cooperative game, and non-cooperative game, to obtain the optimal adjustment strategy while ensuring the individual benefit of participants. Then, the generality and accuracy of classification thresholds are improved via the application of GTRSs. Finally, a case study is conducted on the evaluation of scenic spots via a questionnaire survey, verifying the feasibility and effectiveness of the proposed method.