Detecting Community Structure In Complex Networks

Many complex systems in the world can be modeled by a variety of complex networks. Complex networks which composed by the nodes and edges are the abstract representation of complex systems. The nodes represent different individuals, and the edges reflect the relationship between these individuals. The study of complex networks gets more and more attention in Mathematics, Physics, biology, engineering, management, science and etc.. The growing network of human society also needs to understand the variety of artificial and natural complex networks well. Complex network has become an extremely important and challenge issue in scientific research of the network age, and even called"the new science of networks".More and more research shows that many different networks have very remarkable similarities. One of them is community structure. Detecting community structure in network is more significance for understanding the network structure and analyzing the network properties. There are very extensive applications for analyzing community structure in biology, physic, computer graph, society and many different fields. So, it is Worthwhile to analysis community structure accurately with variety information of network.By researching deeply in community structure, the following work has been introduced in this dissertation.An algorithm for detecting community structure based on shared neighbors is proposed. First, the node with maximum degree is chosen as the first node in the community. Then, this algorithm calculates the numbers of shared neighbors between the community and its neighbors. Finally, the shared neighbors are compared to find the closest node with the community. At the same time, the local modularity will be used to decide whether this node can be added into the community. Doing so, the algorithm will detect the community structure and cluster the network. It is tested in three typical networks, and the results prove the viability and effective of this algorithm.An algorithm for detecting structure in complex network based on local information is proposed. Based on the local information, an algorithm is proposed for discovering the communities in complex networks by introducing the definition of the edge cluster coefficient. To obtain the community structures in the networks, the node with maximum degree in remainder network is first found. And then, some edge cluster coefficients and nodes'degrees are computed to decide whether the nodes connected with the community can be added into the community. At last, the community structure is detected. It is tested at the three group network and the Zachary network, the results show the validity and effective of this algorithm.