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Podobnik B, Majdandzic A, Curme C, et al. Network risk and forecasting power in phase-flipping dynamical networks. Phys Rev E Stat Nonlin Soft Matter Phys. 2014;89(4):042807doi: 10.1103/PhysRevE.89.042807.
Podobnik, B., Majdandzic, A., Curme, C., Qiao, Z., Zhou, W. X., Stanley, H. E., & Li, B. (2014). Network risk and forecasting power in phase-flipping dynamical networks. Physical review. E, Statistical, nonlinear, and soft matter physics, 89(4), 042807. https://doi.org/10.1103/PhysRevE.89.042807
Podobnik, B, et al. "Network risk and forecasting power in phase-flipping dynamical networks." Physical review. E, Statistical, nonlinear, and soft matter physics vol. 89,4 (2014): 042807. doi: https://doi.org/10.1103/PhysRevE.89.042807
Podobnik B, Majdandzic A, Curme C, Qiao Z, Zhou WX, Stanley HE, Li B. Network risk and forecasting power in phase-flipping dynamical networks. Phys Rev E Stat Nonlin Soft Matter Phys. 2014 Apr;89(4):042807. doi: 10.1103/PhysRevE.89.042807. Epub 2014 Apr 15. PMID: 24827293.
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Phys Rev E Stat Nonlin Soft Matter Phys. 2014 Apr;89(4):042807. doi: 10.1103/PhysRevE.89.042807. Epub 2014 Apr 15.

Network risk and forecasting power in phase-flipping dynamical networks.

Physical review. E, Statistical, nonlinear, and soft matter physics

B Podobnik, A Majdandzic, C Curme, Z Qiao, W-X Zhou, H E Stanley, B Li

Affiliations

  1. Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts 02215, USA and Faculty of Civil Engineering, University of Rijeka, 51000 Rijeka, Croatia and Zagreb School of Economics and Management, 10000 Zagreb, Croatia and Faculty of Economics, University of Ljubljana, 1000 Ljubljana, Slovenia.
  2. Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts 02215, USA.
  3. NUS Graduate School for Integrative Sciences and Engineering, NUS, Singapore 117456, Singapore and Department of Physics and Center for Computational Science and Engineering, NUS, Singapore 117546, Singapore.
  4. School of Business, School of Science, and Research Center for Econophysics, East China University of Science and Technology, Shanghai 200237, China.
  5. NUS Graduate School for Integrative Sciences and Engineering, NUS, Singapore 117456, Singapore and Department of Physics and Center for Computational Science and Engineering, NUS, Singapore 117546, Singapore and Center for Phononics and Thermal Energy Science, School of Physics Science and Engineering, Tongji University, Shanghai 200092, People's Republic of China.

PMID: 24827293 DOI: 10.1103/PhysRevE.89.042807

Abstract

To model volatile real-world network behavior, we analyze a phase-flipping dynamical scale-free network in which nodes and links fail and recover. We investigate how stochasticity in a parameter governing the recovery process affects phase-flipping dynamics, and we find the probability that no more than q% of nodes and links fail. We derive higher moments of the fractions of active nodes and active links, fn(t) and fℓ(t), and we define two estimators to quantify the level of risk in a network. We find hysteresis in the correlations of fn(t) due to failures at the node level, and we derive conditional probabilities for phase-flipping in networks. We apply our model to economic and traffic networks.

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