ScholarBank@NUShttps://scholarbank.nus.edu.sgThe DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Sun, 01 Aug 2021 05:13:17 GMT2021-08-01T05:13:17Z5051- Self-affine fractals embedded in spectra of complex networkshttps://scholarbank.nus.edu.sg/handle/10635/97881Title: Self-affine fractals embedded in spectra of complex networks
Authors: Yang, H.; Yin, C.; Zhu, G.; Li, B.
Abstract: The scaling properties of spectra of real world complex networks are studied by using the wavelet transform. It is found that the spectra of networks are multifractal. According to the values of the long-range correlation exponent, the Hust exponent H, the networks can be classified into three types, namely, H>0.5, H=0.5, and H
Fri, 18 Apr 2008 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/978812008-04-18T00:00:00Z
- Uncovering evolutionary ages of nodes in complex networkshttps://scholarbank.nus.edu.sg/handle/10635/98511Title: Uncovering evolutionary ages of nodes in complex networks
Authors: Zhu, G.-M.; Yang, H.J.; Yang, R.; Ren, J.; Li, B.; Lai, Y.-C.
Abstract: In a complex network, different groups of nodes may have existed for different amounts of time. To detect the evolutionary history of a network is of great importance. We present a spectral-analysis based method to address this fundamental question in network science. In particular, we find that there are complex networks in the real-world for which there is a positive correlation between the eigenvalue magnitude and node age. In situations where the network topology is unknown but short time series measured from nodes are available, we suggest to uncover the network topology at the present (or any given time of interest) by using compressive sensing and then perform the spectral analysis. Knowledge of ages of various groups of nodes can provide significant insights into the evolutionary process underpinning the network. It should be noted, however, that at the present the applicability of our method is limited to the networks for which information about the node age has been encoded gradually in the eigen-properties through evolution. © 2012 EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg.
Thu, 01 Mar 2012 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/985112012-03-01T00:00:00Z
- Heat flux distribution and rectification of complex networkshttps://scholarbank.nus.edu.sg/handle/10635/96771Title: Heat flux distribution and rectification of complex networks
Authors: Liu, Z.; Wu, X.; Yang, H.; Gupte, N.; Li, B.
Abstract: It was recently found that the heterogeneity of complex networks can enhance transport properties such as epidemic spreading, electric energy transfer, etc. A trivial deduction would be that the presence of hubs in complex networks can also accelerate the heat transfer although no concrete research has been done so far. In the present study, we have studied this problem and have found a surprising answer: the heterogeneity does not favor but prevents the heat transfer. We present a model to study heat conduction in complex networks and find that the network topology greatly affects the heat flux. The heat conduction decreases with the increase of heterogeneity of the network caused by both degree distribution and the clustering coefficient. Its underlying mechanism can be understood by using random matrix theory. Moreover, we also study the rectification effect and find that it is related to the degree difference of the network, and the distance between the source and the sink. These findings may have potential applications in real networks, such as nanotube/nanowire networks and biological networks. © IOP Publishing Ltd and Deutsche Physikalische Gesellschaft.
Thu, 11 Feb 2010 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/967712010-02-11T00:00:00Z
- Complexities of human promoter sequenceshttps://scholarbank.nus.edu.sg/handle/10635/96043Title: Complexities of human promoter sequences
Authors: Zhao, F.; Yang, H.; Wang, B.
Abstract: By means of the diffusion entropy approach, we detect the scale-invariance characteristics embedded in the 4737 human promoter sequences. The exponent for the scale-invariance is in a wide range of [0.3, 0.9], which centered at δc = 0.66. The distribution of the exponent can be separated into left and right branches with respect to the maximum. The left and right branches are asymmetric and can be fitted exactly with Gaussian form with different widths, respectively. © 2007 Elsevier Ltd. All rights reserved.
Tue, 21 Aug 2007 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/960432007-08-21T00:00:00Z
- Localizations on complex networkshttps://scholarbank.nus.edu.sg/handle/10635/97086Title: Localizations on complex networks
Authors: Zhu, G.; Yang, H.; Yin, C.; Li, B.
Abstract: We study the structural characteristics of complex networks using the representative eigenvectors of the adjacent matrix. The probability distribution function of the components of the representative eigenvectors are proposed to describe the localization on networks where the Euclidean distance is invalid. Several quantities are used to describe the localization properties of the representative states, such as the participation ratio, the structural entropy, and the probability distribution function of the nearest neighbor level spacings for spectra of complex networks. Whole-cell networks in the real world and the Watts-Strogatz small-world and Barabasi-Albert scale-free networks are considered. The networks have nontrivial localization properties due to the nontrivial topological structures. It is found that the ascending-order-ranked series of the occurrence probabilities at the nodes behave generally multifractally. This characteristic can be used as a structural measure of complex networks. © 2008 The American Physical Society.
Mon, 23 Jun 2008 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/970862008-06-23T00:00:00Z