Research On Key Technologies Of Public Opinion Dissemination Based On Complex Networks

With the rapid development of the Internet,social networks have changed the way people communicate and communicate in a new way and provide people with a broader and free platform.The freedom of speech in social networks has increased people's participation in public events,deepened the transparency of social information,and narrowed the distance between people.People's opinions and opinions made social networks a major battlefield reflecting public opinion and public opinion.At this time,all kinds of information are rapidly fermented,forming a network sensation,and the source of social network information is more complicated,and the cost of rumoring is also very low.The speed of lyric communication is fast,and the scope of proliferation is wide,which increases the difficulty of supervision of relevant departments and affects social stability.When an emergency occurs,the public opinion begins to spread rapidly.If it is not handled properly,it may even trigger a public opinion crisis.Using the theory and technology of complex networks,quantitative analysis and research on public opinion transmission in complex social networks can be carried out.By studying the public opinion propagation model,network topology analysis and network structure change trend in complex networks,it has important theoretical basis and practical significance for supervision and correct guidance of public opinion.It is of great significance to study the diffusion method of online public opinion for us to understand the public opinion communication process and individual interaction mode,and to formulate reasonable countermeasures.The infectious disease model is a common one when using complex network technology to establish a public opinion propagation model.In view of the fact that the traditional infectious disease model is too singular to accurately describe the complex trends of public opinion,this paper proposes a MI-SEIR model,which considers two modes of public opinion communication: media communication and interpersonal relationship,and the propagation nodes are different.The opinions are divided into three categories.According to the parameters such as the node's own properties,the evolution law of the viewpoint value is established,and the category of the node can be changed according to the evolution of the viewpoint value.The simulation results show that each parameter has a certain degree of influence on the public opinion propagation process.Existing community discovery methods based on complex networks mostly use network structure information to divide the network.Aiming at the problem that the globalbased community discovery method is too complicated and the local-based community discovery method is too low,this paper proposes a community-based algorithm based on core nodes.First,the core nodes are selected based on the node degree and the local clustering coefficient to calculate the priority of all nodes in the network.Secondly,the multi-node node similarity is used to judge whether other nodes and core nodes can be divided into the same small group,and finally the small groups with greater degree of compactness are merged.The simulation results show that the accuracy of the algorithm is high.Real networks are mostly directed and the network structure changes dynamically over time.Traditional link prediction methods are mostly applicable to undirected networks,and their analysis methods cannot effectively mine information in real networks.Aiming at the above problems,this paper proposes a calculation based on the NALAS index of time impact factor,considering the influence of network directionality and network history structure,and simulating the algorithm on multiple data sets.The results show that compared with other algorithms,the prediction accuracy of the proposed algorithm is improved.