scholarly journals Synchronization transition in Sakaguchi-Kuramoto model on complex networks with partial degree-frequency correlation

2019 ◽  
Vol 29 (1) ◽  
pp. 013123 ◽  
Author(s):  
Prosenjit Kundu ◽  
Pinaki Pal
Keyword(s):  
Complex Networks ◽  
Kuramoto Model ◽  
Frequency Correlation ◽  
Synchronization Transition ◽  
Partial Degree

scholarly journals Modular synchronization in complex networks with a gauge Kuramoto model

2008 ◽  
Vol 83 (6) ◽  
pp. 68003 ◽  
Author(s):  
E. Oh ◽  
C. Choi ◽  
B. Kahng ◽  
D. Kim
Keyword(s):  
Complex Networks ◽  
Kuramoto Model

A new spectral coarse-graining algorithm based on K-means clustering in complex networks

2019 ◽  
Vol 33 (01) ◽  
pp. 1850421 ◽  
Author(s):  
Lang Zeng ◽  
Zhen Jia ◽  
Yingying Wang
Keyword(s):  
Complex Networks ◽  
Large Scale ◽  
Dynamic Properties ◽  
Coarse Graining ◽  
Kuramoto Model ◽  
Coarse Grained ◽  
Original Network ◽  
Topological Information ◽  
Real World Applications ◽  
Large Scale Networks

Coarse-graining of complex networks is one of the important algorithms to study large-scale networks, which is committed to reducing the size of networks while preserving some topological information or dynamic properties of the original networks. Spectral coarse-graining (SCG) is one of the typical coarse-graining algorithms, which can keep the synchronization ability of the original network well. However, the calculation of SCG is large, which limits its real-world applications. And it is difficult to accurately control the scale of the coarse-grained network. In this paper, a new SCG algorithm based on K-means clustering (KCSCG) is proposed, which cannot only reduce the amount of calculation, but also accurately control the size of coarse-grained network. At the same time, KCSCG algorithm has better effect in keeping the network synchronization ability than SCG algorithm. A large number of numerical simulations and Kuramoto-model example on several typical networks verify the feasibility and effectiveness of the proposed algorithm.

Extraction algorithm for optimal coarse-grained networks on complex networks

2019 ◽  
Vol 30 (11) ◽  
pp. 1950081
Author(s):  
Lang Zeng ◽  
Zhen Jia ◽  
Yingying Wang
Keyword(s):  
Complex Networks ◽  
Network Science ◽  
Dynamic Properties ◽  
Coarse Graining ◽  
Kuramoto Model ◽  
Coarse Grained ◽  
Urgent Problem ◽  
Original Network ◽  
Topological Information ◽  
Extraction Algorithm

Coarse-graining of complex networks is a hot topic in network science. Coarse-grained networks are required to preserve the topological information or dynamic properties of the original network. Some effective coarse-graining methods have been proposed, while an urgent problem is how to obtain coarse-grained network with optimal scale. In this paper, we propose an extraction algorithm (EA) for optimal coarse-grained networks. Numerical simulation for EA on four kinds of networks and performing Kuramoto model on optimal coarse-grained networks, we find our algorithm can effectively obtain the optimal coarse-grained network.

Effects of topological characteristics on rhythmic states of the D-dimensional Kuramoto model in complex networks

2022 ◽  
Vol 32 (1) ◽  
pp. 013118
Author(s):  
Xiang Ling ◽  
Wen-Bin Ju ◽  
Ning Guo ◽  
Kong-Jin Zhu ◽  
Chao-Yun Wu ◽  
...  
Keyword(s):  
Complex Networks ◽  
Kuramoto Model ◽  
Topological Characteristics

scholarly journals The Kuramoto model in complex networks

2016 ◽  
Vol 610 ◽  
pp. 1-98 ◽  
Author(s):  
Francisco A. Rodrigues ◽  
Thomas K. DM. Peron ◽  
Peng Ji ◽  
Jürgen Kurths
Keyword(s):  
Complex Networks ◽  
Kuramoto Model ◽  
The Kuramoto Model
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