74 citations · 254 across the 7 of their papers we have counts for
7 papers
Sync and swarm: solvable model of non-identical swarmalators
S. Yoon, K. P. O'Keeffe, J. F. F. Mendes +1
We study a model of non-identical swarmalators, generalizations of phase oscillators that both sync in time and swarm in space. The model produces four collective states: asynchron…
Impact of field heterogeneity on the dynamics of the forced Kuramoto model
S. Yoon, E. A. P. Wright, J. F. F. Mendes +1
We studied the impact of field heterogeneity on entrainment in a system of uniformly interacting phase oscillators. Field heterogeneity is shown to induce dynamical heterogeneity i…
Topological phase transition in the periodically forced Kuramoto model
E. A. P. Wright, S. Yoon, J. F. F. Mendes +1
A complete bifurcation analysis of explicit dynamical equations for the periodically forced Kuramoto model was performed in [L. M. Childs and S. H. Strogatz. Chaos 18 , 043128 (200…
Structural stability of interaction networks against negative external fields
S. Yoon, A. V. Goltsev, J. F. F. Mendes
We explore structural stability of weighted and unweighted networks of positively interacting agents against a negative external field. We study how the agents support the activity…
Synchronization in the random field Kuramoto model on complex networks
M. A. Lopes, E. M. Lopes, S. Yoon +2
We study the impact of random pinning fields on the emergence of synchrony in the Kuramoto model on complete graphs and uncorrelated random complex networks. We consider random fie…
Critical behavior of the relaxation rate, the susceptibility, and a pair correlation function in the Kuramoto model on scale-free networks
S. Yoon, M. Sorbaro Sindaci, A. V. Goltsev +1
We study the impact of network heterogeneity on relaxation dynamics of the Kuramoto model on uncorrelated complex networks with scale-free degree distributions. Using the Ott-Anton…
Belief-propagation algorithm and the Ising model on networks with arbitrary distributions of motifs
S. Yoon, A. V. Goltsev, S. N. Dorogovtsev +1
We generalize the belief-propagation algorithm to sparse random networks with arbitrary distributions of motifs (triangles, loops, etc.). Each vertex in these networks belongs to a…