arXiv subjects

Ying-Di Jin

Publications and source records attributed to Ying-Di Jin.

4 recordsLinked to original sources

arXiv2006

Power-law Strength-Degree Correlation From a Resource-Allocation Dynamics on Weighted Networks

Many weighted scale-free networks are known to have a power-law correlation between strength and degree of nodes, which, however, has not been well explicated. We investigate the dynamic behaviors of resource/traffic flow on scale-free networks. The dynamical system will evolve to a kinetic equilibrium state, where the strength, defined by the amount of resource or traffic load, is correlated with the degree in a power-law form with tunable exponent. The analytical results agree with simulations well.

physics.soc-ph↗

arXiv2004

Clustering Evolutionary Stock Market Model

As a typical representation of complex networks studied relatively thoroughly, financial market presents some special details, such as its nonconservation and opinions spreading. In this model, agents congregate to form some clusters, which may grow or collapse with the evolution of the system. To mimic an open market, we allow some ones participate in or exit the market suggesting that the number of the agents would fluctuate. Simulation results show that the large events are frequent in the fluctuations of the stock price generated by the artificial stock market when compared with a normal process and the price return distribution is a \emph{lévy} distribution in the central part followed by an approximately exponential truncation.

cond-mat.other↗

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↗

arXiv2004

A weighted evolving network model more approach to reality

In search of many social and economical systems, it is found that node strength distribution as well as degree distribution demonstrate the behavior of power-law with droop-head and heavy-tail. We present a new model for the growth of weighted networks considering the connection of nodes with low strengths. Numerical simulations indicate that this network model yields three power-law distributions of the node degrees, node strengths and connection weights. Particularly, the droop-head and heavy-tail effects can be reflected in the first two ones by this new model.

cond-mat.dis-nn↗