arXiv subjects

Yan-Zhong Dang

Publications and source records attributed to Yan-Zhong Dang.

7 recordsLinked to original sources

arXiv2006

Relationship Between Structural Characters and Synchronizability of Scale-free Networks

Using Memory Tabu Search(MTS) algorithm, we investigate the relationship between structural characters and synchronizability of scale-free networks by maximizing and minimizing the ratio $Q$ of the eigenvalues of the coupling matrix by edge-intercrossing procedures. The numerical results indicate that clustering coefficient $C$, maximal betweenness $B_{max}$ are two most important factors to scale-free network synchronizability, and assortative coefficient $r$ and average distance $D$ are the secondary ones. Moreover, the average degree $ $ affects the relationship between above structural characters and synchronizability of scale-free networks, and the minimal $Q$ decreases when $ $ increases.

physics.data-an↗

arXiv2006

Weighted Network of Chinese Nature Science Basic Research

Using the requisition papers of Chinese Nature Science Basic Research in management and information department, we construct the weighted network of research areas({\bf WRAN}) represented by the subject codes. In WRAN, two research areas are considered connected if they have been filled in at least one requisition paper. The edge weight is defined as the number of requisition papers which have filled in the same pairs of codes. The node strength is defined as the number of requisition papers which have filled in this code, including the papers which have filled in it only. Here we study a variety of nonlocal statistics for these networks, such as typical distances between research areas through the network, and measures of centrality such as betweenness. These statistics characteristics can illuminate the global development trend of Chinese scientific study, it is also helpful to adjust the code system to reflect the real status more accurately. Finally, we present a plausible model for the formation and structure of networks with the observed properties.

physics.soc-ph↗

arXiv2005

Self-learning Mutual Selection Model for Weighted Networks

In this paper, we propose a self-learning mutual selection model to characterize weighted evolving networks. By introducing the self-learning probability $p$ and the general mutual selection mechanism, which is controlled by the parameter $m$, the model can reproduce scale-free distributions of degree, weight and strength, as found in many real systems. The simulation results are consistent with the theoretical predictions approximately. Interestingly, we obtain the nontrivial clustering coefficient $C$ and tunable degree assortativity $r$, depending on the parameters $m$ and $p$. The model can unify the characterization of both assortative and disassortative weighted networks. Also, we find that self-learning may contribute to the assortative mixing of social networks.

physics.soc-ph↗

arXiv2005

Multistage Random Growing Small-World Networks with Power-law degree Distribution

In this paper, a simply rule that generates scale-free networks with very large clustering coefficient and very small average distance is presented. These networks are called {\bf Multistage Random Growing Networks}(MRGN) as the adding process of a new node to the network is composed of two stages. The analytic results of power-law exponent $γ=3$ and clustering coefficient $C=0.81$ are obtained, which agree with the simulation results approximately. In addition, the average distance of the networks increases logarithmical with the number of the network vertices is proved analytically. Since many real-life networks are both scale-free and small-world networks, MRGN may perform well in mimicking reality.

physics.comp-ph↗

arXiv2005

A directed network model for World-Wide Web

In this paper, a directed network model for world-wide web is presented. The out-degree of the added nodes are supposed to be scale-free and its mean value is $m$. This model exhibits small-world effect, which means the corresponding networks are of very short average distance and highly large clustering coefficient. More interesting, the in-degree distribution obeys the power-law form with the exponent $γ=2+1/m$, depending on the average out-degree. This finding is supported by the empirical data, which has not been emphasized by the previous studies on directed networks.

physics.soc-ph↗

arXiv2005

Optimization of scale-free network for random failures

It has been found that the networks with scale-free distribution are very resilient to random failures. The purpose of this work is to determine the network design guideline which maximize the network robustness to random failures with the average number of links per node of the network is constant. The optimal value of the distribution exponent and the minimum connectivity to different network size are given in this paper. Finally, the optimization strategy how to improve the evolving network robustness is given.

cond-mat.stat-mech↗

arXiv2005

Optimization of robustness of scale-free network to random and targeted attacks

The scale-fee networks, having connectivity distribution $P(k)\sim k^{-α}$ (where $k$ is the site connectivity), is very resilient to random failures but fragile to intentional attack. The purpose of this paper is to find the network design guideline which can make the robustness of the network to both random failures and intentional attack maximum while keeping the average connectivity $ $ per node constant. We find that when $ =3$ the robustness of the scale-free networks reach its maximum value if the minimal connectivity $m=1$, but when $ $ is larger than four, the networks will become more robust to random failures and targeted attacks as the minimal connectivity $m$ gets larger.

cond-mat.stat-mech↗