title: Reconstructing propagation networks with temporal similarity.
abstract: Node similarity significantly contributes to the growth of real networks. In this paper, based on the observed epidemic spreading results we apply the node similarity metrics to reconstruct the underlying networks hosting the propagation. We find that the reconstruction accuracy of the similarity metrics is strongly influenced by the infection rate of the spreading process. Moreover, there is a range of infection rate in which the reconstruction accuracy of some similarity metrics drops nearly to zero. To improve the similarity-based reconstruction method, we propose a temporal similarity metric which takes into account the time information of the spreading. The reconstruction results are remarkably improved with the new method.
authors.affiliation: 1] Guangdong Province Key Laboratory of Popular High Performance Computers, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, P. R. China [2] Institute of Information Economy, Alibaba Business School, Hangzhou Normal University, Hangzhou 310036, P. R. China [3] Department of Physics, University of Fribourg, Chemin du Musée 3, CH-1700 Fribourg, Switzerland.
authors.forename: Hao
authors.initials: H
authors.lastname: Liao
authors.affiliation: 1] School of Systems Science, Beijing Normal University, Beijing 100875, P. R. China [2] Institute of Information Economy, Alibaba Business School, Hangzhou Normal University, Hangzhou 310036, P. R. China.
authors.forename: An
authors.initials: A
authors.lastname: Zeng
mesh_terms: D008962:Models, Theoretical; D016571:Neural Networks, Computer