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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 590794, 7 pages
http://dx.doi.org/10.1155/2015/590794
Research Article

Complex Networks: Statistical Properties, Community Structure, and Evolution

1School of Computer Science, Communication University of China, Beijing 100024, China
2Engineering Center of Digital Audio and Video, Communication University of China, Beijing 100024, China

Received 26 October 2014; Accepted 31 December 2014

Academic Editor: Yang Tang

Copyright © 2015 Lei Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We investigate the function for different networks based on complex network theory. In this paper, we choose five data sets from various areas to study. In the study of Chinese network, scale-free effect and hierarchical structure features are found in this complex system. These results indicate that the discovered features of Chinese character structure reflect the combination nature of Chinese characters. In addition, we study the community structure in Chinese character network. We can find that community structure is always considered as one of the most significant features in complex networks, and it plays an important role in the topology and function of the networks. Furthermore, we cut all the nodes in the different networks from low degree to high degree and then obtain many networks with different scale. According to the study, two interesting results have been obtained. First, the relationship between the node number of the maximum communities and the number of communities in the corresponding networks is studied and it is linear. Second, when the number of nodes in the maximum communities is increasing, the increasing tendency of the number of its edges slows down; we predict the complex networks have sparsity. The study effectively explains the characteristic and community structure evolution on different networks.