Spectral coarse graining for random walks in bipartite networks
- Wang, Yang Department of Systems Science, School of Management, Beijing Normal University, China - Center for Complexity Research, Beijing Normal University, China
- Zeng, An Department of Physics, University of Fribourg, Switzerland
- Di, Zengru Department of Systems Science, School of Management, Beijing Normal University, China - Center for Complexity Research, Beijing Normal University, China
- Fan, Ying Department of Systems Science, School of Management, Beijing Normal University, China - Center for Complexity Research, Beijing Normal University, China
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07.01.2013
Published in:
- Chaos: An Interdisciplinary Journal of Nonlinear Science. - 2013, vol. 23, no. 1, p. 013104–013104–7
English
Many real-world networks display a natural bipartite structure, yet analyzing and visualizing large bipartite networks is one of the open challenges in complex network research. A practical approach to this problem would be to reduce the complexity of the bipartite system while at the same time preserve its functionality. However, we find that existing coarse graining methods for monopartite networks usually fail for bipartite networks. In this paper, we use spectral analysis to design a coarse graining scheme specific for bipartite networks, which keeps their random walk properties unchanged. Numerical analysis on both artificial and real-world networks indicates that our coarse graining can better preserve most of the relevant spectral properties of the network. We validate our coarse graining method by directly comparing the mean first passage time of the walker in the original network and the reduced one.
- 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 32547
- DOI 10.1063/1.4773823
- Persistent URL
- https://folia.unifr.ch/unifr/documents/303046
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