基于个性化参数学习的社会网络大群体分类决策方法
A Classification Decision Making Method for Large Groups Based on Personalized Parameter Learning Under Social Network Context
李胜利 1舒婷 2魏翠萍 3桑钰政2
作者信息
- 1. 太原师范学院数学与统计学院,晋中 030619;智能优化计算与区块链技术山西省重点实验室,晋中 030619
- 2. 太原师范学院数学与统计学院,晋中 030619
- 3. 扬州大学数学科学学院,扬州 225002
- 折叠
摘要
针对社会网络环境下的大群体分类决策问题,首先,通过构建个性化相似度阈值学习模型,将相似度与社会网络融合,得到修正后的社会网络;然后,利用子网络分割算法将社会网络划分为具有一定特征的子网络,并形成对应的子群,通过DeGroot模型计算子群的群体偏好;接着,在群体偏好聚合过程中,综合偏好顺序一致性程度、子群内聚力及其成员数量,并通过参数学习计算三个指标的最优权重分配,进而计算子群权重.最后,通过一个算例来验证方法的有效性和可行性.
Abstract
In order to solve the problem of large group classification decision-making in social network environment,firstly,we construct a personalized similarity thresh-old learning model,integrate the similarity with social network,and get the modified social network;then,we use sub-network segmentation algorithm to group decision-makers,and compute the group preferences of subgroups through DeGroot model;next,we integrate the degree of consistency of the preference order,cohesion of sub-groups and the number of their members in the process of aggregation of group preferences,and compute the optimal weight assignment of three indicators through parameter learning,and then compute the weights of subgroups.Secondly,in the process of group preference aggregation,we combine the degree of preference order consistency,subgroup cohesion and its number of members,and calculate the optimal weight assignment of the three indexes through parameter learning to compute the subgroup weights.Finally,the validity and feasibility of the method are verified by an example.
关键词
社会网络/信任关系/多指标/个性化相似度阈值Key words
Social network/trust relationship/multiple indicators/personalized sim-ilarity threshold引用本文复制引用
基金项目
国家自然科学基金(71981190)
山西省科技创新人才专项(202204051002018)
山西省哲学社会科学规划项目(2022YJ111)
出版年
2024
NETL
NSTL
万方数据