Solving the inverse problem of noise-driven dynamic networks
Abstract
Nowadays, massive amounts of data are available for analysis in natural and social systems and the tasks to depict system structures from the data, i.e., the inverse problems, become one of the central issues in wide interdisciplinary fields. In this paper, we study the inverse problem of dynamic complex networks driven by white noise. A simple and universal inference formula of double correlation matrices and noise-decorrelation (DCMND) method is derived analytically, and numerical simulations confirm that the DCMND method can accurately depict both network structures and noise correlations by using available output data only. This inference performance has never been regarded possible by theoretical derivation, numerical computation, and experimental design.
- Publication:
-
Physical Review E
- Pub Date:
- January 2015
- DOI:
- 10.1103/PhysRevE.91.012814
- arXiv:
- arXiv:1407.0218
- Bibcode:
- 2015PhRvE..91a2814Z
- Keywords:
-
- 89.75.Hc;
- 05.10.Gg;
- 05.45.Tp;
- Networks and genealogical trees;
- Stochastic analysis methods;
- Time series analysis;
- Physics - Biological Physics;
- Quantitative Biology - Quantitative Methods
- E-Print:
- 4 pages, 2 figures