Effects of accelerating growth on the evolution of weighted complex networks |
| |
| Authors: | Zhongzhi Zhang Lujun Fang Shuigeng Zhou Jihong Guan |
| |
| Affiliation: | 1. School of Computer Science, Fudan University, Shanghai 200433, China;2. Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai 200433, China;3. Department of Computer Science and Technology, Tongji University, 4800 Cao’an Road, Shanghai 201804, China;1. Boston University School of Management, Department of Information Systems, Boston, MA, United States;2. Massachusetts General Hospital, Department of Neurology, United States;3. Harvard Medical School, Boston, MA, United States;1. School of Systems Science and Center for Complexity Research, Beijing Normal University, Beijing, China;2. State Key Laboratory of Power System, Department of Electrical Engineering, Tsinghua University, Beijing, China;3. School of Economics & Management, Southwest Jiaotong University, Chengdu, China;4. School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China;1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, PR China;2. National Key Laboratory of Air Traffic Flow Management, Nanjing 210016, PR China;3. College of Science, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, PR China;4. John von Neumann Institute - Vietnam National University, Ho Chi Minh City, Viet Nam |
| |
| Abstract: | Many real systems possess accelerating statistics where the total number of edges grows faster than the network size. In this paper, we propose a simple weighted network model with accelerating growth. We derive analytical expressions for the evolutions and distributions for strength, degree, and weight, which are relevant to accelerating growth. We also find that accelerating growth determines the clustering coefficient of the networks. Interestingly, the distributions for strength, degree, and weight display a transition from scale-free to exponential form when the parameter with respect to accelerating growth increases from a small to large value. All the theoretical predictions are successfully contrasted with numerical simulations. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
