Research On The Diffusion Mechanism Of True And False Information In Complex Networks

In recent years,the rapid development of computer and mobile Internet technology has greatly changed the way people get information.The rapid development of online social network platform makes information dissemination more convenient.This also provides a way for the spread of rumors,which may cause political and economic harm.Based on the propagation model of true and false information,this paper studies the dynamic evolution process of true and false information propagation from the perspective of the ability of filtering false information in the network,and puts forward the calculation method of the ability of true and false information propagation based on dynamic programming.The main research contents are as follows:1.Self-learning mechanism: This study aims at the ability of learning from historical information of individuals in social networks.Based on this,we propose a new information diffusion model of social networks,which considers two types of nodes:intelligent nodes and normal nodes,as well as two types of information,namely true and false information consumption,and self-learning mechanism.Based on the definition of information filtering ability(IFA),we find that the self-learning mechanism can make the network more intelligent and distinguish the true and false information better.The introduction of smart nodes leads to the social stratification in the chain network,that is,the original real messages released by nodes close to smart nodes can be forwarded to more other nodes.In addition,we find that the interconnection between two networks can make the bridge nodes have higher social influence.2.The estimation method of true and false message propagation ability based on dynamic programming: This study proposes an estimation method of true and false message propagation ability based on dynamic programming,which includes two steps: finding the shortest path and calculating the propagation probability.A large number of experiments show that the estimation method can be used to analyze the information transmission ability and influence of different networks,and solve the problem of uncertainty of experiment repetition and low time efficiency based on the traditional Monte Carlo method.This study provides new insights for controlling the spread of public opinion,building a smart society and analyzing the optimal network structure.3.The impact of community structure on the spread of true and false messages:This study aims at the propagation dynamics of real and fake messages in the network with community structure.We propose a novel model of true and false message propagation,which contains three types of nodes in the network,and explore the impact of community structure on the true and false message propagation.These effects include propagation rate and propagation range.We prove that in the global scope,the significant modular structure of network features strongly affects the spread range and speed of real and false messages in different communities.Our research shows that the Internet water force may try to achieve the fastest communication process with the goal of modularized social networks with good structure,so as to maximize the number of informed audiences,resulting in serious consequences.