Epidemic spreading on complex networks with general degree and weight distributions.
The spread of disease on complex networks has attracted wide attention in the physics community. Recent works have demonstrated that heterogeneous degree and weight distributions have a significant influence on the epidemic dynamics. In this study, a novel edge-weight-based compartmental approach is developed to estimate the epidemic threshold and epidemic size (final infected density) on networks with general degree and weight distributions, and a remarkable agreement with numerics is obtained. Even in complex networks with the strong heterogeneous degree and weight distributions, this approach is used. We then propose an edge-weight-based removal strategy with different biases and find that such a strategy can effectively control the spread of epidemic when the highly weighted edges are preferentially removed, especially when the weight distribution of a network is extremely heterogenous. The theoretical results from the suggested method can accurately predict the above removal effectiveness.
Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, China.
Wei
W
Wang
Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, China and Center for Atmospheric Remote Sensing(CARE), Kyungpook National University, Daegu 702-701, South Korea.
Ming
M
Tang
School of Mathematical Science, Anhui University, Hefei 230039, China.
Hai-Feng
HF
Zhang
Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, China.
Hui
H
Gao
Department of Mathematics, Kyungpook National University, Daegu 702-701, South Korea.
Younghae
Y
Do
Department of Physics, East China Normal University, Shanghai 200062, China.
Zong-Hua
ZH
Liu