Cumulative Laws, Team Assembling Mechanisms Determining Network Structure
WU Bin1, LIU Qi2, YE Qi1
1Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 1008762Department of Probability and Statistics, School of Mathematical Sciences, Peking University, Beijing 100871
Cumulative Laws, Team Assembling Mechanisms Determining Network Structure
WU Bin1;LIU Qi2;YE Qi1
1Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 1008762Department of Probability and Statistics, School of Mathematical Sciences, Peking University, Beijing 100871
摘要A number of researching works have shed light on the field of complex networks recently. We investigate a wide range of real-world networks and find several interesting phenomena. Firstly, almost all of these networks evolve by overlapping new small graphs on former networks. Secondly, not only the degree sequence of the mature network follows a power-law distribution, but also the distribution of the cumulative occurrence times during the growing process are revealed to have a heavy tail. Existing network evolving models do not provide interpretation to these phenomena. We suggest a model based on the team assembling mechanism, which is extracted from the growing processes of real-world networks and requires simple parameters, and produces networks exhibiting these properties observed in the present study and in previous works.
Abstract：A number of researching works have shed light on the field of complex networks recently. We investigate a wide range of real-world networks and find several interesting phenomena. Firstly, almost all of these networks evolve by overlapping new small graphs on former networks. Secondly, not only the degree sequence of the mature network follows a power-law distribution, but also the distribution of the cumulative occurrence times during the growing process are revealed to have a heavy tail. Existing network evolving models do not provide interpretation to these phenomena. We suggest a model based on the team assembling mechanism, which is extracted from the growing processes of real-world networks and requires simple parameters, and produces networks exhibiting these properties observed in the present study and in previous works.
[1] Barab{\'asi A L, Albert R 1999 Science 286509 [2] Guimera R, Uzzi B, Spiro J and Amaral A L N 2005 Science 308 697 [3] Leskovec J, Kleinberg J and Faloutsos C 2007 ACMTrans. Knowl. Discov. Data 1 2 [4] Oliveira J G and Barab{\'asi A L 2005 Nature 437 1251
2008, Vol. 25 
