The Discovery Of Important Nodes In The Complex Network Of Communication Studies

As is known to all, complicated network widely exists in nature, biological, engineering and field of human society. In-depth study of complex networks can reveal a large number of complex common law hidden in systems.At this stage, the complex network has entered into the high-intensity cross-border Mas hup state, including mathematics, physics, biology, computing science, network science,and so on.the propagation model research of complex network originally began in the1960s, a mass media model mostly draws the infectious disease model due to the opinion spread and transmission of the virus, similar to the diffusion, which was proposed firstly.The core node is an important control means of spread at home and abroad,but there are more static data analysis than dynamic analysis for the spread and control at home and abroad.A great deal of scholars developed many improved model based on the study of network evolution model’s generation mechanism.Evolution model of network can not only capture the dynamic generated by the network, but also has great significance to study the rationality of design and structure characteristics of the actual network. According to the expand and shrink effect due to the information dissemination, identify the key point in the information dissemination process can be well applied in the field of public opinion, control, advertising effects, and the spread of the virus control.The main work of this paper is studying the discovery of important nodes in the complex network, reads as follows:1.Introduced complex network evolution in the study of history as well as the related basic theory, summed up the typical propagation model algorithm and gives comparative analysis, besides that, obtained the application scope of these algorithms.2.In this paper, aiming at the deficiency existing in community discovery algorithm model, proposed a new improved algorithm. Namely, the edge clustering coefficient that based on the research of simple graph community structure discovery. Confirmed by the experiment of the two data sets that the time complexity is greatly reduced due to the improved edge aggregation algorithm3.Based on micro bo information platform,using the ROST and SPSS software to analyze, such as the user’s similarly topics, mutual forwarding comments, concern and attention, to make clustering divided, and to identify the interested circles "achieve dynamic data analysis of complex networks. By the way,the experimental results compared with the actual situation has a strong anastomosis.