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Sooyeon Yoon

Publications and source records attributed to Sooyeon Yoon.

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Condensation phenomena of conserved-mass aggregation model on weighted complex networks

We investigate the condensation phase transitions of conserved-mass aggregation (CA) model on weighted scale-free networks (WSFNs). In WSFNs, the weight $w_{ij}$ is assigned to the link between the nodes $i$ and $j$. We consider the symmetric weight given as $w_{ij}=(k_i k_j)^α$. In CA model, the mass $m_i$ on the randomly chosen node $i$ diffuses to a linked neighbor of $i$,$j$, with the rate $T_{ji}$ or an unit mass chips off from the node $i$ to $j$ with the rate $ωT_{ji}$. The hopping probability $T_{ji}$ is given as $T_{ji}= w_{ji}/\sum_{ } w_{li}$, where the sum runs over the linked neighbors of the node $i$. On the WSFNs, we numerically show that a certain critical $α_c$ exists below which CA model undergoes the same type of the condensation transitions as those of CA model on regular lattices. However for $α\geq α_c$, the condensation always occurs for any density $ρ$ and $ω$. We analytically find $α_c = (γ-3)/2$ on the WSFN with the degree exponent $γ$. To obtain $α_c$, we analytically derive the scaling behavior of the stationary distribution $P^{\infty}_k$ of finding a walker at nodes with degree $k$, and the probability $D(k)$ of finding two walkers simultaneously at the same node with degree $k$. We find $P^{\infty}_k \sim k^{α+1-γ}$ and $D(k) \sim k^{2(α+1)-γ}$ respectively. With $P^{\infty}_k$, we also show analytically and numerically that the average mass $m(k)$ on a node with degree $k$ scales as $k^{α+1}$ without any jumps at the maximal degree of the network for any $ρ$ as in the SFNs with $α=0$.

cond-mat.stat-mech

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