摘要
针对群体社交网络舆情演化时,目前方法获取关键节点中的数据较为困难,导致无法准确获得舆情传播次数、搜索指数、达到舆情峰值所用时间等参数,存在演化精度低的问题,提出基于聚类算法与易感-感染-易感(SIS:Susceptible Infected Susceptible Model)模型的群体社交网络舆情演化仿真方法。在群体社交网络中采用PageRank算法获取关键节点,利用聚类算法对关键节点中的数据聚类进行处理,在此基础上构建SIS模型,并通过其完成群体社交网络的舆情演化仿真。实验结果表明,该方法可准确地获得舆情传播次数、搜索指数、达到舆情峰值所用时间等参数,演化仿真精度高。
During the evolution of public opinion in group social networks,it is difficult for the current methods to obtain the data in key nodes,resulting in the inability to accurately obtain parameters such as the number of public opinion propagation,search index,time to reach the peak of public opinion,and the problem of low evolution accuracy.A simulation method of public opinion evolution in group social network based on clustering algorithm and SIS(Susceptible Infected Susceptible Model)model is proposed.The PageRank algorithm is used to obtain the key nodes,and the clustering algorithm is used to cluster the data in the key nodes.The SIS model is constructed,and the public opinion evolution simulation of the group social network is completed through the SIS model.The experimental results show that the proposed method can accurately obtain the parameters such as the number of public opinion propagation,search index and the time to reach the peak of public opinion,and the evolutionary simulation accuracy is high.
作者
路苗
门可
马永红
张海瑞
冯彦成
LU Miao;MEN Ke;MA Yonghong;ZHANG Hairui;FENG Yancheng(School of Public Health,Xi'an Medical University,Xi'an 710021,China)
出处
《吉林大学学报（信息科学版）》
CAS
2023年第1期106-111,共6页
Journal of Jilin University（Information Science Edition）
基金
陕西省教育厅基金资助项目(21JZ049)。
关键词
聚类算法
SIS模型
关键节点识别
PAGERANK算法
网络舆情演化
clustering algorithm
susceptible infected susceptible(SIS)model
key node identification
PageRank algorithm
evolution of network public opinion
