title: Predicting the evolution of spreading on complex networks.
abstract: 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.
authors.affiliation: 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.
authors.forename: Duan-Bing
authors.initials: DB
authors.lastname: Chen
authors.affiliation: Department of Physics, University of Fribourg, Fribourg CH1700, Switzerland.
authors.forename: Rui
authors.initials: R
authors.lastname: Xiao
authors.affiliation: 1] Department of Physics, University of Fribourg, Fribourg CH1700, Switzerland [2] School of Systems Science, Beijing Normal University - Beijing 100875, P. R. China.
authors.forename: An
authors.initials: A
authors.lastname: Zeng