Inferring network topology via the propagation process
- Zeng, An Department of Physics, University of Fribourg, Switzerland
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01.11.2013
Published in:
- Journal of Statistical Mechanics: Theory and Experiment. - 2013, vol. 2013, no. 11, p. P11010
English
Inferring the network topology from the dynamics is a fundamental problem, with wide applications in geology, biology, and even counter-terrorism. Based on the propagation process, we present a simple method to uncover the network topology. A numerical simulation on artificial networks shows that our method enjoys a high accuracy in inferring the network topology. We find that the infection rate in the propagation process significantly influences the accuracy, and that each network corresponds to an optimal infection rate. Moreover, the method generally works better in large networks. These finding are confirmed in both real social and nonsocial networks. Finally, the method is extended to directed networks, and a similarity measure specific for directed networks is designed.
- Faculty
- Faculté des sciences et de médecine
- Department
- Département de Physique
- Language
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- English
- Classification
- Physics
- License
- License undefined
- Identifiers
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- RERO DOC 209585
- DOI 10.1088/1742-5468/2013/11/P11010
- Persistent URL
- https://folia.unifr.ch/unifr/documents/303458
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