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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
Department
Physique
Language
  • English
Classification
Physics
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/304466
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