Research Of Community Structure In Complex Networks

This paper studies the complex network model of the network with community structure and the division of community structure in complex networks. The main contents are as follows:Firstly, this paper studies the graph characteristics of the complex networks. The theory of graph is always the most important method and also is the basic theory in research of complex networks.Three important statistical characteristics of complex networks are mentioned here and they arc the basis for modeling complex networks.According to these characteristics, some typical complex networks are introduced and finally the BA model is deeply analyzed.Secondly, this paper studies the modeling problem of complex networks with community structure and it is respectively discussed in weighted network and un-weighted network. There are some discussion about the advantages and disadvantages of these algorithms and a new algorithm of complex networks with community structure is proposed.Thirdly, this paper studies the problem of division of community structure in complex network, which is a hot issue in complex network in recently year. Generally, construction algorithms for the community structure networks are mainly divided into two ways. The first one is what is based on image segmentation algorithms and the other on is based on hierarchical clustering from the sociology. They respectively have their own advantages and disadvantages. The community structure network algorithm requires a large amount of network data set with community structure. As a result, this paper proposed a new algorithm to build community network and build also a corresponding experimental platform is given. This can bring a convience to research of modeling and found of community structure in complex networks.Finally, a new kind of community structure division algorithm is discussed in this paper. Previous studis have shown that the node degree distribution of the scale-free network follows power law distribution. According to this, a new weight function for the node from un-weighted network is built and makes use of this measure function, a new multi-community identification algorithm for the scale-free network is constructed to identify the community number and the community ownership of every node.