Group relationship discovery of virtual social platforms aims to obtain the relationship of tar-get network social groups from personal network track and public opinion data,and reflect complex re-lationships across regions and ethnic groups.Firstly,a closed-loop structure of group relationship dis-covery of virtual social platforms based on the network community discovery requirements is pro-posed.The structure of input and output data,and information flow chart of the model are designed to meet the classification requirements of network platform virtual community test sets.Then,based on community structure detection in multi-relationships social networks(CSDM)clustering algorithm,the activity information of virtual accounts is analyzed theoretically,and the network behavior trajecto-ry rules of active users are extracted.Finally,machine learning community detection method(MLCDM)is used to mine important community attributes of virtual network in test sets,and analy-sis on simulation results is given.
news and public opinion/community discovery/machine learning community detection method(MLCDM)/cluster fusion/community structure detection in multi-relationships social net-works(CSDM)
维普
万方数据