Complex Network Research, The Nature And Transmission Dynamics Behavior

In recent ten years, research of complex network has achieved a lot in many aspects, such as modeling topology of networks, analyzing character of networks, dynamics on networks, synchronization and control of networks and empirical study of networks.With consideration of topology effecting information propagation, the relationship of data mining and complex network is discussed, and the relevant data mining algorithm is studied: through improving the structure of the FP-TREE, an algorithm of mining sequential patterns is proposed, which is the higher efficiency. Ensuring accuracy of classification, according to introducing TSVM distance threshold to predict, a semi-supervised learning method to improve the efficiency of classification is proposed. A new similarity method of nodes is proposed for community detection of complex networks. By transferring from topology similarity to vector similarity of nodes, community detection turns out to spatial data clustering. Two types of information propagation of nodes, diffusion and absorbing spreading, are defined. Consistency between information spreading and information vector of node and convergence of information spreading under these two types propagation are testified.By adopting clustering algorithms in data mining for testifying method of information vector used in community detection, efficiency of this method is proved. For inefficiency of community detection in large networks by using information vector method mentioned above, a new algorithm based on single information spreading is proposed. Typical empirical networks are used to testify its validity.Analyzing complexity of transportation networks in twelve cities of our country, community detection in those networks are archived by single information spreading mentioned above.Complexity of node state is studied and mechanism of propagation and interaction and infection are defined when multi-information spreading. Multi-information propagation model Sln with inhibiting function is proposed. The results of this model are investigated analytically and by simulations.Based on analyzing and summarizing concept and development of complex network, a variation spreading model caused by contact among of three messages and named CM model, is proposed, it has been analyzed that system transition, the dynamic process of information transmission with the different spreading rate and variation rate, and the effect with community structure.Spreading of information affects the formation of topology of networks. By analyzing the coevolving and interaction between information propagation and formation of network topology, a dynamic growth network model is proposed. In the model, the links of a new node depends on the state and degree of nodes. On this model, dynamics of and on this network are studied.