Key Techniques Of Large-scale Complex Network Information Propagation Platform

The propagation of public opinion and infectious disease has a certain influence or threat on the stability development of the society. Therefore, it is of great significance to accurately analyze and forecast the trend of propagation effectively for the relevant departments to formulate corresponding measures. Currently, researches on the propagation patterns of public opinion and infectious disease mainly include: modeling the mode, modeling and forecasting the trend and analysis of the propagation phenomenon based on complex networks and so on. Although there are many complex network analysis tools which can study the propagation at home and abroad. With the development of the society and the explosive growth of the information network size, it requires the network analysis tool to analyze the propagation on complex networks in an even larger scale. Therefore, we try to construct the large-scale complex network information propagation platform, called LNP for short, as the experimental environment for the study and forecast of propagation law. In this paper, we studied the key technologies of LNP platform, laid solid foundation for the construction of LNP platform. The main works in this paper can be summarized as follows:1. The design of the large-scale complex network information propagation(LNP) platformA detailed and feasible design scheme is an important premise of the construction of LNP platform. In this paper, we have analyzed the current complex network analysis tools, and then selected Igraph as the foundation of the development of LNP platform.2. The analysis of statistical feature of complex networks based on the LNP platformThe analysis of the statistical features of complex networks is the foundation of the further research. In this paper we have studied the demand of academic study on the complex network and the basic analysis function of the existing analysis tools, and then realized the analysis functions of basic statistical characteristics for complex networks on the LNP platform. In order to explain the function of LNP platform, we have experimented on the kinship network which is abstracted from the population database of H Province as an example.3. The rewiring algorithms of complex networksLNP platform have realized the classical random rewiring(RRW) algorithm and greedy rewiring(GRW) algorithm. For further study, we have introduced the probability thought to the rewiring algorithm initiatively, and proposed the degree-preserving greedy probability rewiring algorithm, called GPRW algorithm for short. And then the core idea has been expounded in the view of the undirected and directed complex network. The experiments have been conformed completely, which verified the availability and effectiveness of GPRW algorithm.4. The propagation modelsWe have achieved three classical propagation models: the SI model, the SIS model and SIR model on the LNP platform. The SI model and SIR model were conformed on different networks, which proved the LNP platform’s functions of the study of propagation phenomenon and law.