Network structure and user mental trait-based information spreading prediction system and method

Abstract

The invention discloses a network structure and user mental trait-based information spreading prediction system and method, and belongs to the field of online social network analysis. The method comprises four parts of: obtaining a data source, establishing user side relationship dynamics, establishing user attention evolution dynamics, and constructing a hot topic spreading prediction model. Themethod comprises the following steps of: 1, obtaining a data source; 2, establishing user side relationship dynamics, and carrying out improvement on the user side relationship dynamics by adoption ofgraph evolution game by taking a heterogeneous mean field theory as basis, so as to ensure that the user side relationship dynamics has real applicability; 3, establishing user attention evolution dynamics, defining an earning matrix and an information popularity, and constructing a user attention evolution dynamic mechanism according to evolution game; and 4, constructing an information spreading prediction method. Through combining the user side relationship dynamics and the user attention evolution dynamics with the traditional epidemic disease model, a new information spreading predictionmethod is constructed. The system and method are capable of effectively predicting the dynamic situations of information spreading in complicated social networks, and revealing influences, on information spreading, of different dynamic elements.

Description

technical field [0001] The invention belongs to the intersection field of online social network analysis and complex network, relates to online social network information dissemination prediction, and analyzes the impact of user psychological characteristics and heterogeneity of network structure on information dissemination. Background technique [0002] In recent years, the rapid development of online social networks has provided us with massive real data on the diffusion of information in social networks, which has brought new opportunities for the study of complex network communication dynamics. The research on these real data has great application value, and has attracted many scholars from complex network, sociology, computer science and other fields, thus making social network information dissemination one of the hot areas of current research and exploration. Through the research on the dynamics of information dissemination in social networks, the results can be furth...

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Problems solved by technology

[0004] In addition, adopting a fixed infection rate in the SIR-based information dissemination model is tantamount to ignoring the fact that changes in the popularity of hot topics will lead to changes in the willingness of social network users to participate in topics, which in turn will lead to dynamic changes in the infection rate.

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