arXiv subjects

Soon-Hyung Yook

Publications and source records attributed to Soon-Hyung Yook.

17 recordsLinked to original sources

arXiv2022

A simple and efficient model for epidemic control on multiplex networks

When an unprecedented infectious disease with high mortality and transmissibility emerges, immediate usage of vaccines or medicines is hardly available. Thus, many health authorities rely on non-pharmaceutical interventions through traceable fixed contacts. However, in reality, there is an additional type of transmission routes to the regular and fixed contacts: the random anonymous infection cases where non-pharmaceutical interventions are hardly feasible. In our study, such realistic situations are implemented by the susceptible-infected-recovered model with isolation on multiplex networks. The multiplex networks are composed of a fixed interaction layer and a layer with time-varying random interactions to represent the different types of disease spreading routes. The multiplex networks represent the combinations of the quenched disorder and annealed disorder. Here, we suggest a preemptive isolation protocol which isolates the second nearest neighbors of the hospitalized individuals and compare it with one of the most popular protocol adopted by many health organizations over the globe. From numerical simulations we find that our preemptive measure significantly reduces both the final epidemic size and the number of the isolated per unit time. Our finding suggests a better non-pharmaceutical intervention which can be adopted to various types of diseases even though the contact tracing is only partially available.

physics.soc-ph↗

arXiv2016

Linear and Optimization Hamiltonians in Clustered Exponential Random Graph Modeling

Exponential random graph theory is the complex network analog of the canonical ensemble theory from statistical physics. While it has been particularly successful in modeling networks with specified degree distributions, a naive model of a clustered network using a graph Hamiltonian linear in the number of triangles has been shown to undergo an abrupt transition into an unrealistic phase of extreme clustering via triangle condensation. Here we study a non-linear graph Hamiltonian that explicitly forbids such a condensation and show numerically that it generates an equilibrium phase with specified intermediate clustering.

cond-mat.dis-nn↗

arXiv2016

Quantifying discrepancies in opinion spectra from online and offline networks

Online social media such as Twitter are widely used for mining public opinions and sentiments on various issues and topics. The sheer volume of the data generated and the eager adoption by the online-savvy public are helping to raise the profile of online media as a convenient source of news and public opinions on social and political issues as well. Due to the uncontrollable biases in the population who heavily use the media, however, it is often difficult to measure how accurately the online sphere reflects the offline world at large, undermining the usefulness of online media. One way of identifying and overcoming the online-offline discrepancies is to apply a common analytical and modeling framework to comparable data sets from online and offline sources and cross-analyzing the patterns found therein. In this paper we study the political spectra constructed from Twitter and from legislators' voting records as an example to demonstrate the potential limits of online media as the source for accurate public opinion mining.

physics.soc-ph↗

arXiv2013

Bayesian Inference of Natural Rankings in Incomplete Competition Networks

Competition between a complex system's constituents and a corresponding reward mechanism based on it have profound influence on the functioning, stability, and evolution of the system. But determining the dominance hierarchy or ranking among the constituent parts from the strongest to the weakest -- essential in determining reward or penalty -- is almost always an ambiguous task due to the incomplete nature of competition networks. Here we introduce ``Natural Ranking," a desirably unambiguous ranking method applicable to a complete (full) competition network, and formulate an analytical model based on the Bayesian formula inferring the expected mean and error of the natural ranking of nodes from an incomplete network. We investigate its potential and uses in solving issues in ranking by applying to a real-world competition network of economic and social importance.

physics.soc-ph↗

arXiv2013

Phase transitions in Paradigm models

In this letter we propose two general models for paradigm shift, deterministic propagation model (DM) and stochastic propagation model (SM). By defining the order parameter $m$ based on the diversity of ideas, $Δ$, we study when and how the transition occurs as a cost $C$ in DM or an innovation probability $α$ in SM increases. In addition, we also investigate how the propagation processes affect on the transition nature. From the analytical calculations and numerical simulations $m$ is shown to satisfy the scaling relation $m=1-f(C/N)$ for DM with the number of agents $N$. In contrast, $m$ in SM scales as $m=1-f(α^a N)$.

physics.soc-ph↗

arXiv2012

Agglomerative percolation on the Bethe lattice and the triangular cactus

We study the agglomerative percolation (AP) models on the Bethe lattice and the triangular cactus to establish the exact mean-field theory for AP. Using the self-consistent simulation method, based on the exact self-consistent equation, we directly measure the order parameter $P_{\infty}$ and average cluster size $S$. From the measured $P_{\infty}$ and $S$ we obtain the critical exponents $β_k$ and $γ_k$ for $k=2$ and 3. Here $β_k$ and $γ_k$ are the critical exponents for $P_\infty$ and $S$ when the growth of clusters spontaneously breaks the $Z_k$ symmetry of the $k$-partite graph (Lau, Paczuski, and Grassberger, 2012). The obtained values are $β_2=1.79(3)$, $γ_2=0.88(1)$, $β_3=1.35(5)$, and $γ_3=0.94(2)$. By comparing these values of exponents with those for ordinary percolation ($β_{\infty}=1$ and $γ_{\infty}=1$) we also find the inequalities between the exponents, as $β_\infty<β_3<β_2$ and $γ_\infty>γ_3>γ_2$. These results quantitatively verify the conjecture that the AP model belongs to a new universality class if $Z_k$ symmetry is broken spontaneously, and the new universality class depends on $k$ [Lau et al., Phys. Rev. E 86, 011118 (2012)].

cond-mat.stat-mech↗

arXiv2012

Bond-site duality and phase transition nature of explosive percolations on a two-dimensional lattice

To establish the bond-site duality of explosive percolations in 2 dimension, the site and bond explosive percolation models are carefully defined on a square lattice. By studying the cluster distribution function and the behavior of the second largest cluster, it is shown that the duality in which the transition is discontinuous exists for the pairs of the site model and the corresponding bond model which relatively enhances the intra-bond occupation. In contrast the intra-bond-suppressed models which have no corresponding site models undergo the continuous transition and satisfy the normal scaling ansatz as ordinary percolation.

cond-mat.stat-mech↗

arXiv2012

Explosive percolations on the Bethe Lattice

Based on the self-consistent equations of the order parameter $P_\infty$ and the mean cluster size $S$, we develop a novel self-consistent simulation (SCS) method for arbitrary percolation on the Bethe lattice (infinite homogeneous Cayley tree). By applying SCS to the well-known percolation models, random bond percolation and bootstrap percolation, we obtain prototype functions for continuous and discontinuous phase transitions. By comparing the key functions obtained from SCSs for the Achlioptas processes (APs) with a product rule and a sum rule to the prototype functions, we show that the percolation transition of AP models on the Bethe lattice is continuous regardless of details of growth rules.

cond-mat.stat-mech↗

arXiv2011

Explosive site percolation with a product rule

We study the site percolation under Achlioptas process (AP) with a product rule in a $2-dimensional$ (2D) square lattice. From the measurement of the cluster size distribution, $P_s$, we find that $P_s$ has a very robust power-law regime followed by a stable hump near the transition threshold. Based on the careful analysis on the $P_s$ distribution, we show that the transition should be discontinuous. The existence of the hysteresis loop in order parameter also verifies that the transition is discontinuous in 2D. Moreover we also show that the transition nature from the product rule is not the same as that from a sum rule in 2D.

cond-mat.stat-mech↗

arXiv2007

Diffusive capture processes for information search

We show how effectively the diffusive capture processes (DCP) on complex networks can be applied to information search in the networks. Numerical simulations show that our method generates only 2% of traffic compared with the most popular flooding-based query-packet-forwarding (FB) algorithm. We find that the average searching time, $ $, of the our model is more scalable than another well known $n$-random walker model and comparable to the FB algorithm both on real Gnutella network and scale-free networks with $γ=2.4$. We also discuss the possible relationship between $ $ and $ $, the second moment of the degree distribution of the networks.

physics.soc-ph↗

arXiv2007

Random walks and diameter of finite scale-free networks

Dynamical scalings for the end-to-end distance $R_{ee}$ and the number of distinct visited nodes $N_v$ of random walks (RWs) on finite scale-free networks (SFNs) are studied numerically. $\left< R_{ee} \right>$ shows the dynamical scaling behavior $\left = \bar{\ell}^α(γ, N) g(t/\bar{\ell}^z)$, where $\bar{\ell}$ is the average minimum distance between all possible pairs of nodes in the network, $N$ is the number of nodes, $γ$ is the degree exponent of the SFN and $t$ is the step number of RWs. Especially, $\left $ in the limit $t \to \infty$ satisfies the relation $\left< R_{ee} \right> \sim \bar{\ell}^α\sim d^α$, where $d$ is the diameter of network with $d ({\bar \ell}) \simeq \ln N$ for $γ\ge 3$ or $d ({\bar \ell}) \simeq \ln \ln N$ for $γ< 3$. Based on the scaling relation $\left< R_{ee} \right>$, we also find that the scaling behavior of the diameter of networks can be measured very efficiently by using RWs.

cond-mat.stat-mech↗

arXiv2006

Statistical properties of sampled networks by random walks

We study the statistical properties of the sampled networks by a random walker. We compare topological properties of the sampled networks such as degree distribution, degree-degree correlation, and clustering coefficient with those of the original networks. From the numerical results, we find that most of topological properties of the sampled networks are almost the same as those of the original networks for $γ\lesssim 3$. In contrast, we find that the degree distribution exponent of the sampled networks for $γ>3$ somewhat deviates from that of the original networks when the ratio of the sampled network size to the original network size becomes smaller. We also apply the sampling method to various real networks such as collaboration of movie actor, world wide web, and peer-to-peer networks. All topological properties of the sampled networks show the essentially same as the original real networks.

physics.soc-ph↗

arXiv2006

Diffusive Capture Process on Complex Networks

We study the dynamical properties of a diffusing lamb captured by a diffusing lion on the complex networks with various sizes of $N$. We find that the life time $ of a lamb scales as \sim N$ and the survival probability $S(N\to \infty,t)$ becomes finite on scale-free networks with degree exponent $γ>3$. However, $S(N,t)$ for $γ<3$ has a long-living tail on tree-structured scale-free networks and decays exponentially on looped scale-free networks. It suggests that the second moment of degree distribution $ is the relevant factor for the dynamical properties in diffusive capture process. We numerically find that the normalized number of capture events at a node with degree $k$, $n(k)$, decreases as $n(k)\sim k^{-σ}$. When $γ<3$, $n(k)$ still increases anomalously for $k\approx k_{max}$. We analytically show that $n(k)$ satisfies the relation $n(k)\sim k^2P(k)$ and the total number of capture events $N_{tot}$ is proportional to $, which causes the $γ$ dependent behavior of $S(N,t)$ and $.

cond-mat.dis-nn↗

arXiv2005

Synchronization of R{ö}ssler Oscillators on Scale-free Topologies

We study the synchronization of R{ö}ssler oscillators as prototype of chaotic systems, when they are coupled on scale-free complex networks. We find that the underlying topology crucially affects the global synchronization properties. Especially, we show that the existence of loops facilitates the synchronizability of the system, whereas Rössler oscillators do not synchronize on tree-like topologies beyond a certain size. By considering Cayley trees, modified by various shortcuts, we find that also the distribution of shortest path lengths between two oscillators plays an important role for the global synchronization.

cond-mat.stat-mech↗

arXiv2005

Self-similar Scale-free Networks and Disassortativity

Self-similar networks with scale-free degree distribution have recently attracted much attention, since these apparently incompatible properties were reconciled in a paper by Song et al. by an appropriate box-counting method that enters the measurement of the fractal dimension. We study two genetic regulatory networks ({\it Saccharomyces cerevisiae} and {\it Escherichai coli} and show their self-similar and scale-free features, in extension to the datasets studied by Song et al. Moreover, by a number of numerical results we support the conjecture that self-similar scale-free networks are not assortative. From our simulations so far these networks seem to be disassortative instead. We also find that the qualitative feature of disassortativity is scale-invariant under renormalization, but it appears as an intrinsic feature of the renormalization prescription, as even assortative networks become disassortative after a sufficient number of renormalization steps.

cond-mat.stat-mech↗

arXiv2004

Multiscaling and non-universality in fluctuations of driven complex systems

For many externally driven complex systems neither the noisy driving force, nor the internal dynamics are a priori known. Here we focus on systems for which the time dependent activity of a large number of components can be monitored, allowing us to separate each signal into a component attributed to the external driving force and one to the internal dynamics. We propose a formalism to capture the potential multiscaling in the fluctuations and apply it to the high frequency trading records of the New York Stock Exchange. We find that on the time scale of minutes the dynamics is governed by internal processes, while on a daily or longer scale the external factors dominate. This transition from internal to external dynamics induces systematic changes in the scaling exponents, offering direct evidence of non-universality in the system.

cond-mat.stat-mech↗

arXiv2001

Modeling the Internet's Large-Scale Topology

Network generators that capture the Internet's large-scale topology are crucial for the development of efficient routing protocols and modeling Internet traffic. Our ability to design realistic generators is limited by the incomplete understanding of the fundamental driving forces that affect the Internet's evolution. By combining the most extensive data on the time evolution, topology and physical layout of the Internet, we identify the universal mechanisms that shape the Internet's router and autonomous system level topology. We find that the physical layout of nodes form a fractal set, determined by population density patterns around the globe. The placement of links is driven by competition between preferential attachment and linear distance dependence, a marked departure from the currently employed exponential laws. The universal parameters that we extract significantly restrict the class of potentially correct Internet models, and indicate that the networks created by all available topology generators are significantly different from the Internet.

cond-mat.stat-mech↗