Research Of Community Detecting And Its Application Based On The Clustering Coefficient

In our real life, many common physical networks can be described andrepresented by complex networks, for example, when we use e-mail network, nodesrepresent receivers and senders in the network, and sending the email each otherrepresents the interaction and relationship between two nodes. There are many similarnetwork exists in real life. So the complex networks is a new interdisciplinaryresearch direction, has attracted wide attention from researchers in different subjectareas. Although the complex networks coming from different systems, but acommonalities existence in different complex networks of systems, which hasimportant significance. in the physical sciences, social sciences and the biologicalsciences. Community structure is an important factor in complex networks, theinternal nodes of community structure have close connection, while nodes betweencommunity structures have the sparse connection. Community structure can describethe characteristic of complex networks. So the accurate analysis and making anintensive study of community structure in complex networks is a very meaningfultopic in the research field of complex networks.At present, there are many kinds of detecting community structure algorithms,this paper introduces a variety of algorithms, such as, spectral bisection method,Kernighan-Lin algorithm, Potts algorithm, GN algorithm, A fast algorithm forNewman,and so on. The paper presents a community detecting algorithm based onnetwork clustering coefficient, this algorithm will be starting from the nodeinformation network, it can divide community structure of the network from the localto the whole. The algorithm is applied in the unweighted network and weightednetwork, and it can quickly and accurately obtain the division result in the unweightednetwork and weighted network of experiments. Finally, according to some data ofindustry departments which is provided by input-output tables in2007economic inChina. The paper can use the algorithm for community detecting in42industries, anddraws the corresponding conclusion.