Research On Influence Modeling And Information Propagation Based On Information Propagation Reliability In Social Networks

With the rapid development of the Internet,social networks have become an important medium for people to obtain information and exchange opinions.The analysis and research on the influence and information propagation reliability is beneficial to the implementation of information diffusion,commodity marketing,advertising and public opinion monitoring and other applications.However,With the increasing size of social network users,the increasing diversity of social network platforms,as well as social users have differences on attribute characteristics and other factors,there are two problems on influence modeling and information propagation based on information propagation reliability: firstly,some algorithms are not considered enough,resulting in low accuracy,and can not achieve good results in complex social networks;secondly,some algorithms are too complex,only applicable to theoretical research,can not run in large-scale real networks.In the research of influence modeling and information propagation,the increasing number of users in social networks requires high efficiency of the algorithm,and the method with high time complexity is not suitable for large-scale networks;the complex user interaction behavior,time and user relationship have higher requirements for influence assessment;and the reliable path serial search algorithm is inefficient and can not be applied to large-scale networks.Therefore,on the basis of analyzing and researching the shortcomings of the existing work,this paper makes use of the factors such as social relations,user attributes and application needs in social networks to model users' influence and search the reliable information propagation paths.The main research contents are as follows:(1)Social influence assessment among users in social networks.Social influence analysis helps quantify the influence of each user and identify the most influential users in social networks.The current influence assessment model does not comprehensively consider the effect of user interaction behavior on the influence,the effect of the time and frequency of tweet release on the influence,and the indirect influence of multi-hop users.This paper introduces fan entropy,tweet entropy and interaction entropy to measure the complexity and uncertainty of social influence,and proposes an evaluation model of direct influence and indirect influence.Quantitative calculation of user influence through given information entropy.The experimental results on the real network datasets show that the entropy-based social influence assessment model proposed in this paper can effectively measure the real influence between users.(2)Reliable information propagation paths finding in social networks.The problem of reliable information propagation paths finding in social networks is to find the most influential propagation paths among users.To maximize influence,information can be disseminated from the information source to a group of influential opinion leaders,so that the influence of opinion leaders can be used to achieve the purpose of publicity and promotion.At present,most of the information propagation research in social networks does not consider how to find the information propagation paths that propagates information from the source node to a group of influential nodes.To establish the most influential information propagation paths,this paper constructs a multicast information propagation model from information source to a group of nodes,and proposes a parallel multicast information propagation algorithm.A lot of experiments have been carried out on real networks and artificial networks.The experimental results show that the algorithm can effectively select the information propagation paths with high influence.