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B. Kahng

Publications and source records attributed to B. Kahng.

At least 127 records · Page 7Linked to original sources
arXiv2004

Sandpile avalanche dynamics on scale-free networks

Avalanche dynamics is an indispensable feature of complex systems. Here we study the self-organized critical dynamics of avalanches on scale-free networks with degree exponent $γ$ through the Bak-Tang-Wiesenfeld (BTW) sandpile model. The threshold height of a node $i$ is set as $k_i^{1-η}$ with $0\leqη<1$, where $k_i$ is the degree of node $i$. Using the branching process approach, we obtain the avalanche size and the duration distribution of sand toppling, which follow power-laws with exponents $τ$ and $δ$, respectively. They are given as $τ=(γ-2 η)/(γ-1-η)$ and $δ=(γ-1-η)/(γ-2)$ for $γ<3-η$, 3/2 and 2 for $γ>3-η$, respectively. The power-law distributions are modified by a logarithmic correction at $γ=3-η$.

cond-mat.stat-mech↗
arXiv2003

On the evolution of scale-free graphs

We study the evolution of random graphs where edges are added one by one between pairs of weighted vertices so that resulting graphs are scale-free with the degree exponent $γ$. We use the branching process approach to obtain scaling forms for the cluster size distribution and the largest cluster size as functions of the number of edges $L$ and vertices $N$. We find that the process of forming a spanning cluster is qualitatively different between the cases of $γ>3$ and $2<γ<3$. While for the former, a spanning cluster forms abruptly at a critical number of edges $L_c$, generating a single peak in the mean cluster size $ $ as a function of $L$, for the latter, however, the formation of a spanning cluster occurs in a broad range of $L$, generating double peaks in $ $.

cond-mat.stat-mech↗
arXiv2003

Self-organized Model for Modular Complex Networks : Division and Independence

We introduce a minimal network model which generates a modular structure in a self-organized way. To this end, we modify the Barabasi-Albert model into the one evolving under the principle of division and independence as well as growth and preferential attachment (PA). A newly added vertex chooses one of the modules composed of existing vertices, and attaches edges to vertices belonging to that module following the PA rule. When the module size reaches a proper size, the module is divided into two, and a new module is created. The karate club network studied by Zachary is a prototypical example. We find that the model can reproduce successfully the behavior of the hierarchical clustering coefficient of a vertex with degree k, C(k), in good agreement with empirical measurements of real world networks.

cond-mat.stat-mech↗
arXiv2003

Sandpile on Scale-Free Networks

We investigate the avalanche dynamics of the Bak-Tang-Wiesenfeld (BTW) sandpile model on scale-free (SF) networks, where threshold height of each node is distributed heterogeneously, given as its own degree. We find that the avalanche size distribution follows a power law with an exponent $τ$. Applying the theory of multiplicative branching process, we obtain the exponent $τ$ and the dynamic exponent $z$ as a function of the degree exponent $γ$ of SF networks as $τ=γ/(γ-1)$ and $z=(γ-1)/(γ-2)$ in the range $2 < γ< 3$ and the mean field values $τ=1.5$ and $z=2.0$ for $γ>3$, with a logarithmic correction at $γ=3$. The analytic solution supports our numerical simulation results. We also consider the case of uniform threshold, finding that the two exponents reduce to the mean field ones.

cond-mat.stat-mech↗
arXiv2003

Probabilistic prediction in scale-free networks: Diameter changes

In complex systems, responses to small perturbations are too diverse to predict how much they would be definitely, and then such diverse responses can be predicted in a probabilistic way. Here we study such a problem in scale-free networks, for example, the diameter changes by the deletion of each node for various in silico and real world scale-free networks. We find that the diameter changes are indeed diverse and exhibit an algebraic decay with an exponent ζasymptotically. Interestingly, the exponent ζis robust as ζ\simeq 2.2(1) for most scale-free networks, insensitive to the degree exponents γas long as 2 < γ\le 3. However, there is another type with ζ\simeq 1.7(1) and its examples include the Internet and its related in silico model.

cond-mat.stat-mech↗
arXiv2003

Kinetic Roughening of Ion-Sputtered Pd(001) Surface: Beyond the Kuramoto-Sivashinsky Model

We investigate the kinetic roughening of Ar$^+$ ion-sputtered Pd(001) surface both experimentally and theoretically. \textit{In situ} real-time x-ray reflectivity and \textit{in situ} scanning tunneling microscopy show that nanoscale adatom islands form and grow with increasing sputter time $t$. Surface roughness, $W(t)$, and lateral correlation length, $ξ(t)$, follows the scaling laws, $W(t)\sim t^β$ and $ξ(t)\sim t^{1/z}$ with the exponents $β\simeq 0.20$ and $1/z\simeq 0.20$, for ion beam energy $ε=0.5$ keV, which is inconsistent with the prediction of the Kuramoto-Sivashinsky (KS) model. We thereby extend the KS model by applying the Sigmund theory of sputter erosion to the higher order, ${\cal O}(\nabla^4, h^2)$, where $h$ is surface height, and derive a new term of the form $\nabla^2(\nabla h)^2$ which plays an indispensable role in describing the observed morphological evolution of the sputtered surface.

cond-mat.stat-mech↗
arXiv2003

The q-component static model : modeling social networks

We generalize the static model by assigning a q-component weight on each vertex. We first choose a component $(μ)$ among the q components at random and a pair of vertices is linked with a color $μ$ according to their weights of the component $(μ)$ as in the static model. A (1-f) fraction of the entire edges is connected following this way. The remaining fraction f is added with (q+1)-th color as in the static model but using the maximum weights among the q components each individual has. This model is motivated by social networks. It exhibits similar topological features to real social networks in that: (i) the degree distribution has a highly skewed form, (ii) the diameter is as small as and (iii) the assortativity coefficient r is as positive and large as those in real social networks with r reaching a maximum around $f \approx 0.2$.

cond-mat.stat-mech↗
arXiv2003

Emerging behavior in electronic bidding

We characterize the statistical properties of a large number of agents on two major online auction sites. The measurements indicate that the total number of bids placed in a single category and the number of distinct auctions frequented by a given agent follow power-law distributions, implying that a few agents are responsible for a significant fraction of the total bidding activity on the online market. We find that these agents exert an un-proportional influence on the final price of the auctioned items. This domination of online auctions by an unusually active minority may be a generic feature of all online mercantile processes.

cond-mat.stat-mech↗
arXiv2003

Identification of essential and functionally moduled genes through the microarray assay

Identification of essential genes is one of the ultimate goals of drug designs. Here we introduce an {\it in silico} method to select essential genes through the microarray assay. We construct a graph of genes, called the gene transcription network, based on the Pearson correlation coefficient of the microarray expression level. Links are connected between genes following the order of the pair-wise correlation coefficients. We find that there exist two meaningful fractions of links connected, $p_m$ and $p_s$, where the number of clusters becomes maximum and the connectivity distribution follows a power law, respectively. Interestingly, one of clusters at $p_m$ contains a high density of essential genes having almost the same functionality. Thus the deletion of all genes belonging to that cluster can lead to lethal inviable mutant efficiently. Such an essential cluster can be identified in a self-organized way. Once we measure the connectivity of each gene at $p_s$. Then using the property that the essential genes are likely to have more connectivity, we can identify the essential cluster by finding the one having the largest mean connectivity per gene at $p_m$.

cond-mat.stat-mech↗
arXiv2002

Classification of scale-free networks

While the emergence of a power law degree distribution in complex networks is intriguing, the degree exponent is not universal. Here we show that the betweenness centrality displays a power-law distribution with an exponent ηwhich is robust and use it to classify the scale-free networks. We have observed two universality classes with η\approx 2.2(1) and 2.0, respectively. Real world networks for the former are the protein interaction networks, the metabolic networks for eukaryotes and bacteria, and the co-authorship network, and those for the latter one are the Internet, the world-wide web, and the metabolic networks for archaea. Distinct features of the mass-distance relation, generic topology of geodesics and resilience under attack of the two classes are identified. Various model networks also belong to either of the two classes while their degree exponents are tunable.

cond-mat.stat-mech↗
arXiv2002

Betweenness centrality correlation in social networks

Scale-free (SF) networks exhibiting a power-law degree distribution can be grouped into the assortative, dissortative and neutral networks according to the behavior of the degree-degree correlation coefficient. Here we investigate the betweenness centrality (BC) correlation for each type of SF networks. While the BC-BC correlation coefficients behave similarly to the degree-degree correlation coefficients for the dissortative and neutral networks, the BC correlation is nontrivial for the assortative ones found mainly in social networks. The mean BC of neighbors of a vertex with BC $g_i$ is almost independent of $g_i$, implying that each person is surrounded by almost the same influential environments of people no matter how influential the person is.

cond-mat.stat-mech↗
arXiv2002

Infinite-Order Percolation and Giant Fluctuations in a Protein Interaction Network

We investigate a model protein interaction network whose links represent interactions between individual proteins. This network evolves by the functional duplication of proteins, supplemented by random link addition to account for mutations. When link addition is dominant, an infinite-order percolation transition arises as a function of the addition rate. In the opposite limit of high duplication rate, the network exhibits giant structural fluctuations in different realizations. For biologically-relevant growth rates, the node degree distribution has an algebraic tail with a peculiar rate dependence for the associated exponent.

cond-mat.stat-mech↗
arXiv2002

Fluctuation-driven dynamics of the Internet topology

We study the dynamics of the Internet topology based on the empirical data on the level of the autonomous systems. It is found that the fluctuations occurring in the stochastic process of connecting and disconnecting edges are important features of the Internet dynamics. The network's overall growth can be described approximately by a single characteristic degree growth rate $g_{\rm eff} \approx 0.016$ and the fluctuation strength $σ_{\rm eff} \approx 0.14$, together with the vertex growth rate $α\approx 0.029$. A stochastic model which incorporate these values and an adaptation rule newly introduced reproduces several features of the real Internet topology such as the correlations between the degrees of different vertices.

cond-mat.stat-mech↗
arXiv2001

A Geometric Fractal Growth Model for Scale Free Networks

We introduce a deterministic model for scale-free networks, whose degree distribution follows a power-law with the exponent $γ$. At each time step, each vertex generates its offsprings, whose number is proportional to the degree of that vertex with proportionality constant m-1 (m>1). We consider the two cases: first, each offspring is connected to its parent vertex only, forming a tree structure, and secondly, it is connected to both its parent and grandparent vertices, forming a loop structure. We find that both models exhibit power-law behaviors in their degree distributions with the exponent $γ=1+\ln (2m-1)/\ln m$. Thus, by tuning m, the degree exponent can be adjusted in the range, $2 <γ< 3$. We also solve analytically a mean shortest-path distance d between two vertices for the tree structure, showing the small-world behavior, that is, $d\sim \ln N/\ln {\bar k}$, where N is system size, and $\bar k$ is the mean degree. Finally, we consider the case that the number of offsprings is the same for all vertices, and find that the degree distribution exhibits an exponential-decay behavior.

cond-mat.stat-mech↗
arXiv2001

Robustness of the in-degree exponent for the world-wide web

We consider a stochastic model for directed scale-free networks following power-laws in the degree distributions in both incoming and outgoing directions. In our model, the number of vertices grow geometrically with time with growth rate p. At each time step, (i) each newly introduced vertex is connected to a constant number of already existing vertices with the probability linearly proportional to the in-degree of a selected vertex, and (ii) each existing vertex updates its outgoing edges through a stochastic multiplicative process with mean growth rate of outgoing edges g and variance $σ^2$. Using both analytic treatment and numerical simulations, we show that while the out-degree exponent $γ_{\rm out}$ depends on the parameters, the in-degree exponent $γ_{\rm in}$ has two distinct values, $γ_{\rm in}=2$ for $p > g$ and 1 for $p < g$, independent of different parameters values. The latter case has logarithmic correction to the power-law. Since the vertex growth rate p is larger than the degree growth rate g for the world-wide web (www) nowadays, the in-degree exponent appears robust as $γ_{\rm in}=2$ for the www.

cond-mat.str-el↗
arXiv2001

Universal Behavior of Load Distribution in Scale-free Networks

We study a problem of data packet transport in scale-free networks whose degree distribution follows a power-law with the exponent $γ$. We define load at each vertex as the accumulated total number of data packets passing through that vertex when every pair of vertices send and receive a data packet along the shortest path connecting the pair. It is found that the load distribution follows a power-law with the exponent $δ\approx 2.2(1)$, insensitive to different values of $γ$ in the range, $2 < γ\le 3$, and different mean degrees, which is valid for both undirected and directed cases. Thus, we conjecture that the load exponent is a universal quantity to characterize scale-free networks.

cond-mat.stat-mech↗
arXiv2001

Monte Carlo Simulation of Sinusoidally Modulated Superlattice Growth

The fabrication of ZnSe/ZnTe superlattices grown by the process of rotating the substrate in the presence of an inhomogeneous flux distribution instead of successively closing and opening of source shutters is studied via Monte Carlo simulations. It is found that the concentration of each compound is sinusoidally modulated along the growth direction, caused by the uneven arrival of Se and Te atoms at a given point of the sample, and by the variation of the Te/Se ratio at that point due to the rotation of the substrate. In this way we obtain a ZnSe$_{1-x}$Te$_x$ alloy in which the composition $x$ varies sinusoidally along the growth direction. The period of the modulation is directly controlled by the rate of the substrate rotation. The amplitude of the compositional modulation is monotonous for small angular velocities of the substrate rotation, but is itself modulated for large angular velocities. The average amplitude of the modulation pattern decreases as the angular velocity of substrate rotation increases and the measurement position approaches the center of rotation. The simulation results are in good agreement with previously published experimental measurements on superlattices fabricated in this manner.

cond-mat.mtrl-sci↗