Structure-oriented prediction in complex networks
- Ren, Zhuo-Ming Alibaba Research Center for Complexity Sciences, Alibaba Business School, Hangzhou Normal University, Hangzhou, China - Department of Physics, University of Fribourg, Switzerland
- Zeng, An School of Systems Science, Beijing Normal University, Beijing, China - Department of Physics, University of Fribourg, Switzerland
- Zhang, Yi-Cheng Department of Physics, University of Fribourg, Switzerland - Alibaba Research Center for Complexity Sciences, Alibaba Business School, Hangzhou Normal University, Hangzhou, China
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03.08.2018
Published in:
- Physics Reports. - 2018, vol. 750, p. 1–51
English
Complex systems are extremely hard to predict due to its highly nonlinear interactions and rich emergent properties. Thanks to the rapid development of network science, our understanding of the structure of real complex systems and the dynamics on them has been remarkably deepened, which meanwhile largely stimulates the growth of effective prediction approaches on these systems. In this article, we aim to review different network-related prediction problems, summarize and classify relevant prediction methods, analyze their advantages and disadvantages, and point out the forefront as well as critical challenges of the field.
- 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 323483
- DOI 10.1016/j.physrep.2018.05.002
- Persistent URL
- https://folia.unifr.ch/unifr/documents/307510
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