TY - JOUR
T1 - Controllability of complex networks
T2 - Choosing the best driver set
AU - Amani, Ali Moradi
AU - Jalili, Mahdi
AU - Yu, Xinghuo
AU - Stone, Lewi
N1 - Publisher Copyright:
© 2018 American Physical Society.
PY - 2018/9/24
Y1 - 2018/9/24
N2 - Identifying the best driver set in a complex network is an unsolved problem in the application of pinning control methods. We choose the eigenratio of the augmented Laplacian matrix, and the best driver set is the subset of nodes providing the most effective pinning control strategy, i.e., the one for which synchronization of the whole network to the reference state is attained over the widest range of the coupling parameter. In this Rapid Communication, we propose a centrality measure based on a sensitivity analysis of the Laplacian matrix of the connection graph to find an approximate solution to this problem. The proposed metric is computationally efficient as it requires only a single eigendecomposition of the Laplacian matrix. Numerical results on a number of sample networks show that the proposed metric has a significantly better accuracy than the currently used heuristics, and in most cases can correctly identify the true optimal set, which is obtainable through a combinatorial search.
AB - Identifying the best driver set in a complex network is an unsolved problem in the application of pinning control methods. We choose the eigenratio of the augmented Laplacian matrix, and the best driver set is the subset of nodes providing the most effective pinning control strategy, i.e., the one for which synchronization of the whole network to the reference state is attained over the widest range of the coupling parameter. In this Rapid Communication, we propose a centrality measure based on a sensitivity analysis of the Laplacian matrix of the connection graph to find an approximate solution to this problem. The proposed metric is computationally efficient as it requires only a single eigendecomposition of the Laplacian matrix. Numerical results on a number of sample networks show that the proposed metric has a significantly better accuracy than the currently used heuristics, and in most cases can correctly identify the true optimal set, which is obtainable through a combinatorial search.
UR - http://www.scopus.com/inward/record.url?scp=85053816264&partnerID=8YFLogxK
U2 - 10.1103/PhysRevE.98.030302
DO - 10.1103/PhysRevE.98.030302
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AN - SCOPUS:85053816264
SN - 2470-0045
VL - 98
JO - Physical Review E
JF - Physical Review E
IS - 3
M1 - 030302
ER -