Chen Duan-Bing, Xiao Rui, Zeng An
1] Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China [2] Department of Physics, University of Fribourg, Fribourg CH1700, Switzerland.
Department of Physics, University of Fribourg, Fribourg CH1700, Switzerland.
Sci Rep. 2014 Aug 18;4:6108. doi: 10.1038/srep06108.
Due to the wide applications, spreading processes on complex networks have been intensively studied. However, one of the most fundamental problems has not yet been well addressed: predicting the evolution of spreading based on a given snapshot of the propagation on networks. With this problem solved, one can accelerate or slow down the spreading in advance if the predicted propagation result is narrower or wider than expected. In this paper, we propose an iterative algorithm to estimate the infection probability of the spreading process and then apply it to a mean-field approach to predict the spreading coverage. The validation of the method is performed in both artificial and real networks. The results show that our method is accurate in both infection probability estimation and spreading coverage prediction.
由于应用广泛，复杂网络上的传播过程已得到深入研究。然而，最基本的问题之一尚未得到很好的解决：基于网络上传播的给定快照预测传播的演变。解决了这个问题，如果预测的传播结果比预期窄或宽，就可以提前加速或减缓传播。在本文中，我们提出了一种迭代算法来估计传播过程的感染概率，然后将其应用于平均场方法来预测传播覆盖范围。该方法在人工网络和真实网络中均进行了验证。结果表明，我们的方法在感染概率估计和传播覆盖预测方面都是准确的。
