Reconstructing propagation networks with temporal similarity

Liao, Hao; Zeng, An

doi:10.1038/srep11404

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Reconstructing propagation networks with temporal similarity

Liao, Hao Department of Physics, University of Fribourg, Switzerland - Institute of Information Economy, Alibaba Business School, Hangzhou Normal University, Hangzhou, China - Guangdong Province Key Laboratory of Popular High Performance Computers, College of Computer Science and Software Engineering, Shenzhen University, China
Zeng, An Institute of Information Economy, Alibaba Business School, Hangzhou Normal University, Hangzhou, China - School of Systems Science, Beijing Normal University, Beijing, China

18.06.2015

Published in:

Scientific Reports. - 2015, vol. 5, p. 11404

English 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.

Faculty

Faculté des sciences et de médecine

Department

Département de Physique

Language