The Research Of Traffic Congestion Analysis And Weighted Community Identification Based On Complex Networks

Complex networks have penetrated into many fields such as mathematical subject, bionomics, engineering and so on. Now the research on complex networks has become a very challenging subject. With the rapid development of computer technology and the wide application of network, higher demand for the research on complex networks has been proposed. At present, great progress has been obtained in network topological structure and modeling, epidemic spreading, community structure, network searching, synchronization and so on. Also there are still many problems in these fields.The primary contributions of this thesis are listed as follows:Firstly, we numerically investigate the problem of traffic congestion in complex networks through the use of various routing strategies. Three types of complex networks structures, namely Poisson random networks, small-world networks and scale-free networks, are considered.Secondly, Different approaches to community structure identification in weighted networks in terms of sensitivity and computational cost are investigated. These approaches include Weighted Newman Fast (WNF) algorithm and Weighted External Optimization (WEO) algorithm. Extensive numerical experiments have been done to test the algorithms proposed in this thesis on both computer-simulated and real-world networks. The results prove that these algorithms can better solve the problems proposed in this thesis and get more satisfied results.