arXiv subjects

Kyoung Eun Lee

Publications and source records attributed to Kyoung Eun Lee.

4 recordsLinked to original sources

arXiv2007

Power Law in Firms Bankruptcy

We consider the scaling behaviors for fluctuations of the number of Korean firms bankrupted in the period from August 1 2002 to October 28 2003. We observe a power law for the distribution of the number of the bankrupted firms. The Pareto exponent is close to unity. We also consider the daily increments of the number of firms bankrupted. The probability distribution of the daily increments for the firms bankrupted follows the Gaussian distribution in central part and has a fat tail. The tail parts of the probability distribution of the daily increments for the firms bankrupted follow a power law.

physics.soc-ph↗

arXiv2005

Avalanche dynamics of an idealized neuron function in the brain on uncorrelated random scale-free network

We study a simple model for a neuron function in a collective brain system. The neural network is composed of uncorrelated random scale-free network for eliminating the degree correlation of dynamical processes. The interaction of neurons is supposed to be isotropic and idealized. This neuron dynamics is similar to biological evolution in extremal dynamics with isotropic locally interaction but has different time scale. The evolution of neuron spike takes place according to punctuated patterns similar to the avalanche dynamics. We find that the evolutionary dynamics of this neuron function exhibit self-organized criticality which shows power-law behavior of the avalanche sizes. For a given network, the avalanche dynamic behavior is not changed with different degree exponents of networks, $γ\geq 2.4$ and refractory period correspondent to the memory effect, $T_r$. In addition, the avalanche size distributions exhibit the power-law behavior in a single scaling region in contrast to other networks. However, the return time distributions displaying spatiotemporal complexity have three characteristic time scaling regimes.

cond-mat.stat-mech↗

arXiv2005

Universality Class of Bak-Sneppen Model on Scale-Free Network

We study the critical properties of the Bak-Sneppen coevolution model on scale-free networks by Monte Carlo method. We report the distribution of the avalanche size and fractal activity through the branching process. We observe that the critical fitness $f_c (N)$ depends on the number of the node such as $f_c (N) \sim 1/ \log (N)$ for both the scale-free network and the directed scale-free network. Near the critical fitness many physical quantities show power-law behaviors. The probability distribution $P(s)$ of the avalanche size at the critical fitness shows a power-law like $P(s) \sim s^{-τ}$ with $τ=1.80(3)$ regardless of the scale-free network and the directed scale free network. The probability distribution $P_f (t)$ of the first return time also shows a power-law such as $P_f (t) \sim t^{-τ_f}$. The probability distribution of the first return time has two scaling regimes. The critical exponents $τ_f$ are equivalent for both the scale-free network and the directed scale-free network. We obtain the critical exponents as $τ_{f1} =2.7(1)$ at $t < t_c$ and $τ_{f2} = 1.72(3)$ at $ t >t_c$ where the crossover time $t_c \sim 100$. The Bak-Sneppen model on the scale-free network and directed scale-free network shows a unique universality class. The critical exponents are different from the mean-field results. The directionality of the network does not change the universality on the network.

cond-mat.stat-mech↗

arXiv2005

Waiting-time distribution for a stock-market index

We investigate the waiting-time distribution of the absolute return in the Korean stock-market index KOSPI. We define the waiting time as a time interval during which the normalized absolute return remains continuously below a threshold $r_c$. Through an exponential bin plot, we observe that the waiting-time distribution shows power-law behavior, $p_f (t) \sim t^{-β}$, for a range of threshold values. The waiting-time distribution has two scaling regimes, separated by the crossover time $t_c \approx 200$ min. The power-law exponents of the waiting-time distribution decrease when the return time $Δt$ increases. In the late-time regime, $t > t_c$, the power-law exponents are independent of the threshold to within the error bars for fixed return time.

physics.soc-ph↗