Wang Wei, Tang Ming, Zhang Hai-Feng, Gao Hui, Do Younghae, Liu Zong-Hua
Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, China.
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.
Phys Rev E Stat Nonlin Soft Matter Phys. 2014 Oct;90(4):042803. doi: 10.1103/PhysRevE.90.042803. Epub 2014 Oct 6.
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.
疾病在复杂网络上的传播在物理学界引起了广泛关注。最近的研究表明，度和权重分布的异质性对流行病动力学有显著影响。在本研究中，开发了一种基于边权重的新型 compartmental 方法，以估计具有一般度和权重分布的网络上的流行病阈值和流行病规模（最终感染密度），并与数值模拟取得了显著一致。即使在度和权重分布具有强异质性的复杂网络中，也使用了这种方法。然后，我们提出了一种具有不同偏向的基于边权重的移除策略，发现当优先移除高权重边时，这种策略可以有效地控制流行病的传播，特别是当网络的权重分布极其不均匀时。所提方法的理论结果能够准确预测上述移除效果。
