arXiv subjects

Gang Yan

Publications and source records attributed to Gang Yan.

59 records · Page 4Linked to original sources

arXiv2005

General Dynamics of Topology and Traffic on Weighted Technological Networks

For most technical networks, the interplay of dynamics, traffic and topology is assumed crucial to their evolution. In this paper, we propose a traffic-driven evolution model of weighted technological networks. By introducing a general strength-coupling mechanism under which the traffic and topology mutually interact, the model gives power-law distributions of degree, weight and strength, as confirmed in many real networks. Particularly, depending on a parameter W that controls the total weight growth of the system, the nontrivial clustering coefficient C, degree assortativity coefficient r and degree-strength correlation are all in consistence with empirical evidences.

cond-mat.dis-nn↗

arXiv2005

Random Apollonian Networks

In this letter, we propose a simple rule that generates scale-free networks with very large clustering coefficient and very small average distance. These networks are called {\bf Random Apollonian Networks}(RANs) as they can be considered as a variation of Apollonian networks. We obtain the analytic result of power-law exponent $γ=3$ and clustering coefficient $C={46/3}-36\texttt{ln}{3/2}\approx 0.74$, which agree very well with the simulation results. We prove that the increasing tendency of average distance of RAN is a little slower than the logarithm of the number of nodes in RAN. Since many real-life networks are both scale-free and small-world, RANs may perform well in mimicking the reality. The epidemic spreading process is also studied, we find that the diseases spread slower in RANs than BA networks in the early stage of SI process, indicating that the large clustering coefficient may slower the spreading velocity especially in the outbreaks.

cond-mat.dis-nn↗

arXiv2004

Epidemic spread in weighted scale-free networks

In this letter, we investigate the detailed epidemic spreading process in scale-free networks with links' weights that denote familiarity between two individuals and find that spreading velocity reaches a peak quickly then decays in a power-law form. Numerical study exhibits that the nodes with larger strength is preferential to be infected, but the hierarchical dynamics are not clearly found, which is different from the well-known result in unweighed network case. In addition, also by numerical study, we demonstrate that larger dispersion of weight of networks results in slower spreading, which indicates that epidemic spreads more quickly on unweighted scale-free networks than on weighted scale-free networks with the same condition.

cond-mat.stat-mech↗

arXiv2004

Maximal planar networks with large clustering coefficient and power-law degree distribution

In this article, we propose a simple rule that generates scale-free networks with very large clustering coefficient and very small average distance. These networks are called {\bf Random Apollonian Networks}(RAN) as they can be considered as a variation of Apollonian networks. We obtain the analytic results of power-law exponent $γ=3$ and clustering coefficient $C={46/3}-36\texttt{ln}{3/2}\approx 0.74$, which agree very well with the simulation results. We prove that the increasing tendency of average distance of RAN is a little slower than the logarithm of the number of nodes in RAN. Since most real-life networks are both scale-free and small-world networks, RAN may perform well in mimicking the reality. The RAN possess hierarchical structure as $C(k)\sim k^{-1}$ that in accord with the observations of many real-life networks. In addition, we prove that RAN are maximal planar networks, which are of particular practicability for layout of printed circuits and so on. The percolation and epidemic spreading process are also studies and the comparison between RAN and Barabási-Albert(BA) as well as Newman-Watts(NW) networks are shown. We find that, when the network order $N$(the total number of nodes) is relatively small(as $N\sim 10^4$), the performance of RAN under intentional attack is not sensitive to $N$, while that of BA networks is much affected by $N$. And the diseases spread slower in RAN than BA networks during the outbreaks, indicating that the large clustering coefficient may slower the spreading velocity especially in the outbreaks.

cond-mat.stat-mech↗

arXiv2004

Self-Organization Induced Scale-Free Networks

What is the underlying mechanism leading to power-law degree distributions of many natural and artificial networks is still at issue. We consider that scale-free networks emerges from self-organizing process, and such a evolving model is introduced in this letter. At each time step, a new node is added to the network and connect to some existing nodes randomly, instead of "preferential attachment" introduced by Barabási and Albert, and then the new node will connect with its neighbors' neighbors at a fixed probability, which is natural to collaboration networks and social networks of acquaintance or other relations between individuals. The simulation results show that those networks generated from our model are scale-free networks with satisfactorily large clustering coefficient.

cond-mat.stat-mech↗