2013

Authors: 
Yingjie Xia, Institute of Service Engineering, Hangzhou Normal University, Hangzhou, 310036, P. R. China
Yinzuo Zhou, Corresponding author, Institute for Information Economy, Hangzhou Normal University, Hangzhou, 310036, P. R. China
Abstract: 

We study the influence of randomly distributed phase directions of external force in an array of coupled pendula, instead of studying the influence of continuous phase. We find that with the increase of the absolute value of the phase, the chaotic behaviors of the coupled arrays may be controlled and different synchronized patterns can be induced. These results demonstrate that by introducing the randomness of the phase directions, rather than the continuous value of the phase, it can lead to a synchronization in nonlinear systems.

Authors: 
Bin Shen, Innovation Centre for System Science and Big Data, Ningbo Institute of Technology, Zhejiang University, Ningbo, 315100, P. R. China
Yixiao Li, Corresponding author, School of Information, Zhejiang University of Finance and Economics, Hangzhou, 310018, P. R. China
Abstract: 

Most of co-occurrence networks only record co-occurrence relationships between two entities, and ignore the weights of co-occurrence cliques whose size is bigger than two. However, this ignored information may help us to gain insight into the co-occurrence phenomena of systems. In this paper, we analyze co-occurrence networks with clique occurrence information (CNCI) thoroughly. First, we describe the components of CNCIs and discuss the generation of clique occurrence information. And then, to illustrate the importance and usefulness of clique occurrence information, several metrics, i.e.

Authors: 
Zhen-Zhen Wang, Web Mining Lab, Department of Media and Communication, City University of Hong Kong, Tat Chee Avenue Kowloon, Hong Kong SAR, Hong Kong
Jonathan J. H. Zhu, Web Mining Lab, Department of Media and Communication, City University of Hong Kong, Tat Chee Avenue Kowloon, Hong Kong SAR, Hong Kong
Abstract: 

Homophily and preferential attachment are among the most recognized mechanisms of network evolution. Instead of examining the two mechanisms separately, this study considers them jointly in a scholarly collaboration network. Specifically, when a new scholar enters a field, how does he/she choose the first collaborator from the pool of available scholars?

Authors: 
Zhao-Long Hu, Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China
Zhuo-Ming Ren, Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China
Guang-Yong Yang, Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China
Jian-Guo Liu, Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China
Abstract: 

Human contact networks exhibit the community structure. Understanding how such community structure affects the epidemic spreading could provide insights for preventing the spreading of epidemics between communities. In this paper, we explore the spreading of multiple spreaders in community networks. A network based on the clustering preferential mechanism is evolved, whose communities are detected by the Girvan–Newman (GN) algorithm.

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