Research On Modeling Of Social Network Information Dissemination And The Influence Of Node

Mankind is entering the information age, information is becoming an important social asset. With the rapid development of microblogging network and expanding micro-blog user groups, monitoring public opinion and advertising has also been concerned by more people. Currently, the microblogging social network has become a hot network research, dissemination of information as the internal driving force, influence, network research has become a hot current microblogging.This paper selects the most widely used social network sina-microblog as the object Firstly, using web spider to collect the original data and extracted factors affect user behavior and user node forwarding impact assessment metrics related data, to obtain the desired data set; Secondly, according to the data set,We establishment an information dissemination model based on behavior prediction and the analysis of its characteristics, Finally, using the improved Page Rank algorithm to do the node influence assessment.Grab the data is the basis for the entire study. After comparing various web crawler ways features and the need, we select the web spider to collect the data. When we get the specified size of the original data set and according to the needs, the original data has been divided into blog user attributes and content attributes. Where user attributes include a user ID, concerned about the number, the number of fans, microblogging number and whether the authenticated user and the like; microblogging content properties include the creation time, if it contains special symbols, forwarding number, the number of comments and so on. Finally, aim at the key issues of data fetch process especially anti-climb policy issues, perform the analysis and resolution.Since the currently study of the information dissemination focus on the micro-level departure from the social network, forwarding analyze the behavior of individual users, but few studies from the topology of the entire level of the social network information dissemination process modeling. This paper identifies factors that influence the forwarding behavior characteristics- mainly microblog content and user interests, social relationships microblogging users, microblogging text attributes as well as by the number of times a user activation of these four aspects. On the basis of these four categories of characteristics factor analysis, using logistic regression model dichotomous get information forwarded probability between nodes, thereby establishing Weighted prediction based forwarding to the topology information propagation model, given the evolution of the model of growth process, and in the collected data set to validate the experiment. Experimental results show that the constructed model can well reflect the network structure of the network of microblogging, microblogging can better simulate network information dissemination process.On the basis of micro-blog message constructing propagation model, this paper also influence the microblogging network user node evaluation were studied, The importance of improving the evaluation algorithm Page Rank pages to fit the characteristics of the microblogging network, especially for algorithm and scaling mechanism in the voting stage the problems has been improved. the existing news microblogging influence assessment methods, only focus on forwarding number and the number of comments on the news to do news influence assess, however, with the development of the microblogging network, consider forwarding only a few and comments the approach has been not able to accurately measure the influence of the message, it needs for further analysis, to extract more features to better measure the influence of the message. In determining the influence of the size of the measure- mainly dissemination of information, communication and dissemination of the level of activity after the audience the breadth of the Page Rank algorithm is improved, primarily for ignoring the importance of individual differences in ways the average weight of their assigned voting phase will be improvement, and by the level of activity and numerical spread dissemination of the product as the weights assigned PR value, so as to achieve the propagation ability of the node to obtain the desired additional vote. Finally, experimental results on data sets show that the improved algorithm is better than the original Page Rank hit ratio.