Research On Modeling Method Of Social Network Information Propagation Based On Malezk Propagation Mode

With the rapid development of Internet technology,interaction on social networking platforms has become an indispensable part of people’s daily lives,and social networking platforms have facilitated the rapid and wide dissemination of information.Although social networks have narrowed the distance between people,the efficient dissemination of information has enabled some negative information to form an effective dissemination scale in a short period of time and cause bad social impact.Therefore,studying the characteristics of information dissemination in social network platforms and constructing social information communication models can provide basic model support for curbing negative information dissemination strategies,which is of great significance to the study of information dissemination laws.In previous related studies,the modeling methods based on differentiation,differentiation and machine learning did not emphasize the influence of user and information attributes on the propagation process,and too many input features were easy to lead to overfitting.In order to solve these problems,this thesis first analyzes the information dissemination law in social networks,and then proposes the information dissemination mode in social networks that can comprehensively include the relevant characteristics of information communication based on the information dissemination characteristics in social networks and the communication mode of Malezke,and constructs interpersonal communication models and group communication models under their guidance.Finally,two communication models are used to analyze and predict the change of information propagation scale in social networks over time.The main contributions of this thesis are as follows:1.On the basis of collecting a large amount of real social information communication data,the time change law of social information dissemination scale is analyzed,and the cascade of real social information communication is constructed.According to the experimental data,the cascade of information dissemination is constructed,and then the change law of information dissemination scale in social networks with time is analyzed,which provides a basis for the construction of subsequent communication models and the comparison of information dissemination scale prediction results.2.The interpersonal communication mode and interpersonal communication model of information dissemination in social networks are proposed.In particular,in the actual modeling process,the method of predicting the heat of information propagation based on the propagation structure is used to get rid of the shortcomings of explicitly modeling the heat of information propagation in some previous studies.Finally,by comparing and analyzing the experimental results of the propagation model proposed in this thesis with other propagation models,the effectiveness of the propagation modeling method proposed in this thesis is proved.3.The group communication model and group communication model of information dissemination in social networks are proposed.Based on the research of interpersonal communication model,the influence of group factors on the communication process is analyzed.Firstly,according to the shortcomings of the traditional community detection algorithm in group division,a "propagation population clustering algorithm" is proposed to carry out the actual group division,and a group transmission model is further constructed guided by the group transmission mode.Finally,the superiority of the "propagation population clustering algorithm" and the group propagation model is proved by comparative experiments.