Uncertain Information-based Decision Making Methods For Social Network Groups

With the development of information technology,various social networking platforms have greatly promoted the communication ability of the Internet,and the opinion leaders generated through the interaction of community opinions have a great impact on the large-scale decision-making problems,and the social network-based group decision-making methods have emerged.Compared with the traditional group decision problem,which is oriented to the process of reaching consensus on expert decision results or the consistency of expert decision information,the social network group decision problem fully considers the large-scale group decision problem with social network as the entry point.By increasing the number of decision participants,expanding the depth and breadth of decision makers’ social background,aiming at obtaining decision results with higher public acceptance,and using confidence or trustworthiness as a measure of one-way asymmetric trust degree,the social relationship between decision experts is incorporated into the consensus reaching process to effectively improve the consensus reaching efficiency and increase the credibility of consensus results by grouping consensus results aggregation.This paper gives an algorithm to determine the influence of decision experts based on information expectation based on the decision experts’ weight determination method and relies on social network analysis,and furthermore,the two-stage social network group decision making method based on community consensus can effectively solve a class of social network group decision making problems with uncertain information.First,a probabilistic linguistic information entropy is given based on the basic operation of probabilistic linguistic term set,furthermore,a probabilistic linguistic term information expectation is defined based on the probabilistic linguistic term set score function,the dispersion degree of evaluation information of decision experts is fully considered,a gray correlation analysis is applied to calculate the consensus association degree of decision experts,and an expert weight determination method based on the evaluation dispersion degree and consensus association degree is given,and the algorithm is proved by applying arithmetic examples.The feasibility and stability of the algorithm are demonstrated.Secondly,the uncertain evaluation information of decision participants is transformed and aggregated,and a method for quantifying uncertain decision information based on the hesitant fuzzy term set transformation aggregation operator is given,and the link network of decision participants’ trust relationship is constructed based on the correlation degree between the information expectation vectors of decision participants,and further the social network of decision participants is divided by the influence propagation model,and the weight proportion of decision participants’ community is calculated.The two-stage social network group decision algorithm based on the trust linkage of decision participants and community consensus is given,and the feasibility and stability of the algorithm are demonstrated by applying examples.Finally,the full paper is summarized and summarized,explaining the problems solved by the research content as well as the shortcomings and deficiencies,and providing an outlook on the future research directions.