Modeling And Analysis Of Group Influence In Social Network

People's forms of socializing changed by the rapid development of Online Social Network.However,due to the openness,anonymity,freedom of information releasing and convergence of the social network,it is becoming a platform for public opinion information.It is groups facilitated by curiosity or conformity that lead public opinion spreading quickly.Public malicious opinion and rumors are inseparable fueled by the groups in social network,and group influence has attracted researcher attention in recent years.In the perspective of group,some researchers have carried on the research of the information dissemination,the influence maximization and the group phenomenon in the online social network.However,there is no systematic modeling method for group influence.In terms of group and individual,we quantified features and proposed a group influence model based on factor graph.Combining with the Sina Weibo datasets,a group influence model designed to describe the influence of individual in the online social network.The main work includes:First,a double-layer network relation model is proposed.With data crawling framework fine designed,we collecting and organizing online social network data and selecting effective dataset for experiment.Double-layer network relation model proposed for analyzing the topological relations of online social network,and mining the potential group structure based on the Louvain algorithm.At last,we get two kinds groups:the following groups and the interaction groups.Secondly,we proposed a group influence model and applied to individual prediction.Modeling the group influence based on factor graph by measured the attributes of different types of groups and individual on group level.The model considers the effect of group influence from the group level and the individual level comprehensively,and then applies the group influence method to the individual behavior prediction,with a group influence based individual behavior prediction algorithm,GIBP proposing.Finally,experiments carried out based on GIBP using the online social network dataset.After experiments results analyzed,we get the following conclusions:The group influence model proposed in this paper can depict the influence of users more effectively and more accurately,and can find more users' with obvious group behavior.The GIBP algorithm has better experimental results compared with the existing classical algorithms,which proves the rationality and validity of the group influence modeling method proposed in this paper.The research of group influence does not only help to understand group phenomenon and behavior in social network,but also helps to analyze the individual behavior trends in groups.It is more helpful to understand the evolution process of the complex social network,and it will provide reliable for the decision-making problems such as marketing,network public opinion and cyber violence control with making effective use of the causal relationship between the group and the individual.