Modeling method of dynamic social network information propagation model

Abstract

The invention provides a modeling method of a dynamic social network information propagation model. The modeling method of the dynamic social network information propagation model is characterized in that a network element dynamicity and a network structure dynamicity are introduced based on a static society network evolution game model; two kinds of the dynamicity are combined with a evolution game method for performing cooperative evolution game, thereby obtaining a final evolution stable state in information propagation and forming a dynamic social network cooperative evolution model. According to the modeling method, a special background in information propagation is combined. The modeling method is more similar with reality in social network evolution. Furthermore the network element dynamicity and the network structure dynamicity are combined so that the model is more reasonable. The modeling method has advantages of simple principle, clear flow and easy realization. The modeling method improves predication accuracy in network information propagation. Furthermore the modeling method supplies an effective model support in fields of public sentiment controlling, network group event predicating, enterprise advertisement input product propaganda strategy, etc.

Description

technical field [0001] The invention belongs to the field of information dissemination model research, in particular to a modeling method of a dynamic social network information dissemination model. Background technique [0002] In the existing studies on information dissemination models, most of them are based on a fixed social network structure to discuss the information dissemination process. However, for social networks such as online social networks where the network structure is often changing, and a large number of users and associations are added or removed every day, it is very necessary to include the dynamic change process of nodes and edges in the network structure into the modeling. [0003] Most of the early studies on dynamic social networks focused on the following two aspects: on the one hand, the dynamics of network elements, specifically manifested in the dynamic changes of the attribute status of network nodes or edges under a fixed topology network struc...

AI Technical Summary

Problems solved by technology

However, most of these methods assume that the training set and test set data are statistically consistent. There are two problems with this assumption: 1) The results of learning from a specific data set depend on the existing social network structure, which in turn In a dynamically changing environment, research based on these methods may ignore the effective analysis and prediction of the network structure change process; 2) Machine learning methods often ignore the behavior of participants, strategies and interactions among participants. important influence on the evolution of the network structure

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