TY - JOUR

T1 - Error and attack tolerance of synchronization in Hindmarsh-Rose neural networks with community structure

AU - Li, Chun Hsien

AU - Yang, Suh Yuh

N1 - Funding Information:
The authors would like to thank two anonymous referees for their helpful comments and suggestions that improved the paper. This work was partially supported by the National Science Council of Taiwan under the Grants NSC 100-2115-M-017-004-MY2 (Chun-Hsien Li) and NSC 101-2115-M-008-008-MY2 (Suh-Yuh Yang).

PY - 2014/3/28

Y1 - 2014/3/28

N2 - Synchronization is one of the most important features observed in large-scale complex networks of interacting dynamical systems. As is well known, there is a close relation between the network topology and the network synchronizability. Using the coupled Hindmarsh-Rose neurons with community structure as a model network, in this paper we explore how failures of the nodes due to random errors or intentional attacks affect the synchronizability of community networks. The intentional attacks are realized by removing a fraction of the nodes with high values in some centrality measure such as the centralities of degree, eigenvector, betweenness and closeness. According to the master stability function method, we employ the algebraic connectivity of the considered community network as an indicator to examine the network synchronizability. Numerical evidences show that the node failure strategy based on the betweenness centrality has the most influence on the synchronizability of community networks. With this node failure strategy for a given network with a fixed number of communities, we find that the larger the degree of communities, the worse the network synchronizability; however, for a given network with a fixed degree of communities, we observe that the more the number of communities, the better the network synchronizability.

AB - Synchronization is one of the most important features observed in large-scale complex networks of interacting dynamical systems. As is well known, there is a close relation between the network topology and the network synchronizability. Using the coupled Hindmarsh-Rose neurons with community structure as a model network, in this paper we explore how failures of the nodes due to random errors or intentional attacks affect the synchronizability of community networks. The intentional attacks are realized by removing a fraction of the nodes with high values in some centrality measure such as the centralities of degree, eigenvector, betweenness and closeness. According to the master stability function method, we employ the algebraic connectivity of the considered community network as an indicator to examine the network synchronizability. Numerical evidences show that the node failure strategy based on the betweenness centrality has the most influence on the synchronizability of community networks. With this node failure strategy for a given network with a fixed number of communities, we find that the larger the degree of communities, the worse the network synchronizability; however, for a given network with a fixed degree of communities, we observe that the more the number of communities, the better the network synchronizability.

KW - Centrality

KW - Community structure

KW - Complex network

KW - Failure tolerance

KW - Hindmarsh-Rose neuron

KW - Synchronization

UR - http://www.scopus.com/inward/record.url?scp=84903371912&partnerID=8YFLogxK

U2 - 10.1016/j.physleta.2014.02.041

DO - 10.1016/j.physleta.2014.02.041

M3 - 期刊論文

AN - SCOPUS:84903371912

SN - 0375-9601

VL - 378

SP - 1239

EP - 1248

JO - Physics Letters, Section A: General, Atomic and Solid State Physics

JF - Physics Letters, Section A: General, Atomic and Solid State Physics

IS - 18-19

ER -