Temporal Series Analysis Approach to Spectra of Complex Networks

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dc.creatorYang, Huijie
dc.creatorZhao, Fangcui
dc.creatorQi, Longyu
dc.creatorHu, Beilai
dc.date2004-03-23
dc.date.accessioned2026-07-07T02:57:13Z
dc.date.available2026-07-07T02:57:13Z
dc.descriptionThe spacing of nearest levels of the spectrum of a complex network can be regarded as a time series. Joint use of Multi-fractal Detrended Fluctuation Approach (MF-DFA) and Diffusion Entropy (DE) is employed to extract characteristics from this time series. For the WS (Watts and Strogatz) small-world model, there exist a critical point at rewiring probability . For a network generated in the range, the correlation exponent is in the range of . Above this critical point, all the networks behave similar with that at . For the ER model, the time series behaves like FBM (fractional Brownian motion) noise at . For the GRN (growing random network) model, the values of the long-range correlation exponent are in the range of . For most of the GRN networks the PDF of a constructed time series obeys a Gaussian form. In the joint use of MF-DFA and DE, the shuffling procedure in DE is essential to obtain a reliable result. PACS number(s): 89.75.-k, 05.45.-a, 02.60.-x
dc.description10 pages, 9 figures, to appear in PRE
dc.identifierhttps://arxiv.org/abs/cond-mat/0403569
dc.identifierhttp://arxiv.org/abs/cond-mat/0403569
dc.identifierPhys. Rev. E 69, 066104 (2004) (7 pages)
dc.identifierdoi:10.1103/PhysRevE.69.066104
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/23577
dc.subjectStatistical Mechanics
dc.subjectDisordered Systems and Neural Networks
dc.titleTemporal Series Analysis Approach to Spectra of Complex Networks
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