The Metric Model Of Information Dissemination Based On Social Network

In the information age, social network has become the platform for human beings tocreate and share information, meanwhile the effects and influence of informationdissemination in the network is growing fast. Knowing how to effectively predict thescope of the information dissemination, and researching the influence factors during theinformation dissemination, and then revealing the principles between users is beneficial totimely grasp and control the influence range during the information dissemination.This paper, we proposed a quantitative method to measure the social relationshipstrength and social network information transmission capacity based on the analysis of thesocial network model and information transmission mode. A formalization description ofsocial network is provided and several basic concepts are defined. They are the definitionof the direct social relationship, the potential social relationship, the relationship levels ofsocial network and the relationship strength.While the information is spreading in the complicated social network, users get adynamic changed information dissemination probability. So in order to reveal the law ofthe dissemination of information, we built a metric model of social network based on thesocial network direct relationship intensity P, the social network potential relationshipstrength Q and the social network information dissemination capabilities W. Afterwards doa detailed description of the algorithm’s core idea, used to indicate the metric model. Inthe operation system of Windows XP, using C++to realize the algorithm and usingdifferent colors to show the probability of messages that users may receive during theinformation dissemination, then combining the social network information disseminationmetric model, realize the dynamic simulation of the information dissemination process insocial network. Finally, put forward several optimization rules for the metric model toachieve the optimization of social network and reduce the complexity of the modelcalculations.In order to test the science and efficiency of the model, we choose all of the24 students in class of1009, School of Computer Science and Technology, HuazhongUniversity of Science and Technology as experimental object to validate and simulationthe model. By measuring the relationship strength of the class members to calculate thepotential relationship strength between each other, then use the calculated data to analyzethe information dissemination in the class network. Results show that the greater intensityof social relations, the greater probability of network information transmission, it meansthat the network information transmission capacity is stronger.