ABSTRACT
In this paper, we study the controllability of networks with different numbers of communities and various strengths of community structure. By means of simulations, we show that the degree descending pinning scheme performs best among several considered pinning schemes under a small number of pinned nodes, while the degree ascending pinning scheme is becoming more powerful by increasing the number of pinned nodes. It is found that increasing the number of communities or reducing the strength of community structure is beneficial for the enhancement of the controllability. Moreover, it is revealed that the pinning scheme with evenly distributed pinned nodes among communities outperforms other kinds of considered pinning schemes.
ACKNOWLEDGMENTS
We acknowledge the support from German Academic Exchange Service (DAAD), the Alexander von Humboldt Foundation of Germany, the International Research Training Group (IRTG, DFG), the Major State Basic Research Development Program of China (973 Program, Grant No. 2010CB731400), the National Natural Science Foundation of China (Grant No. 61203235), the Key Creative Project of Shanghai Education Community (Grant No. 13ZZ050), the Key Foundation Project of Shanghai (Grant No. 12JC1400400), and the Natural Science Foundation of Shanghai (Grant No. 13ZR1421300).
- 1. A. Pikovsky, M. Rosenblum, and J. Kurths, Synchronization: A Universal Concept in Nonlinear Sciences (Cambridge University Press, Cambridge, England, 2001). Google ScholarCrossref
- 2. S. Boccaletti, J. Kurths, G. Osipov, D. L. Valladares, and C. S. Zhou, Phys. Rep. 366, 1 (2002). https://doi.org/10.1016/S0370-1573(02)00137-0 , Google ScholarCrossref
- 3. A. Arenas, A. Guilera, J. Kurths, Y. Moreno, and C. Zhou, Phys. Rep. 469, 93 (2008). https://doi.org/10.1016/j.physrep.2008.09.002 , Google ScholarCrossref
- 4. M. Guevara and T. Lewis, Chaos 5, 174 (1995). https://doi.org/10.1063/1.166065 , Google ScholarScitation
- 5. G. Weisbuch, G. Deffuant, and F. Amblard, Physica A 353, 555 (2005). https://doi.org/10.1016/j.physa.2005.01.054 , Google ScholarCrossref
- 6. X. Li, X. F. Wang, and G. Chen, IEEE Trans. Circuits Syst. 51, 2074 (2004). https://doi.org/10.1109/TCSI.2004.835655 , Google ScholarCrossref
- 7. F. Sorrentino, M. di Bernardo, F. Garofalo, and G. Chen, Phys. Rev. E 75, 046103 (2007). https://doi.org/10.1103/PhysRevE.75.046103 , Google ScholarCrossref
- 8. F. Sorrentino, Chaos 17, 033101 (2007). https://doi.org/10.1063/1.2743098 , Google ScholarScitation
- 9. C. Wu, Chaos 18, 037103 (2008). https://doi.org/10.1063/1.2944235 , Google ScholarScitation
- 10. Y. Y. Liu, J. J. Slotine, and A. L. Barabasi, Nature 473, 167 (2011). https://doi.org/10.1038/nature10011 , Google ScholarCrossref
- 11. L. Y. Xiang, Z. X. Liu, Z. Q. Chen, F. Chen, and Z. Z. Yuan, Physica A 379, 298 (2007). https://doi.org/10.1016/j.physa.2006.12.037 , Google ScholarCrossref
- 12. Y. Tang, H. Gao, W. Zou, and J. Kurths, PLoS ONE 7, e41375 (2012). https://doi.org/10.1371/journal.pone.0041375 , Google ScholarCrossref
- 13. Q. Miao, Z. Rong, Y. Tang, and J. Fang, Physica A 387, 6225 (2008). https://doi.org/10.1016/j.physa.2008.06.041 , Google ScholarCrossref
- 14. Y. Tang, Z. Wang, W. K. Wong, J. Kurths, and J. Fang, Chaos 21, 025114 (2011). https://doi.org/10.1063/1.3595701 , Google ScholarScitation
- 15. W. Yu, G. Chen, and J. Lv, Automatica 45, 429 (2009). https://doi.org/10.1016/j.automatica.2008.07.016 , Google ScholarCrossref
- 16. Y. Tang and W. K. Wong, IEEE Trans. Neural Networks Learn. Syst. 24, 435 (2013). https://doi.org/10.1109/TNNLS.2012.2236355 , Google ScholarCrossref
- 17. Y. Tang, Z. Wang, H. Gao, S. Swift, and J. Kurths, IEEE/ACM Trans. Computat. Biol. Bioinf. 9, 1569 (2012). https://doi.org/10.1109/TCBB.2012.124 , Google ScholarCrossref
- 18. Y. Tang, H. Gao, J. Kurths, and J. Fang, IEEE Trans. Ind. Inform. 8, 828 (2012). https://doi.org/10.1109/TII.2012.2187911 , Google ScholarCrossref
- 19. G. W. Flake, S. R. Lawrence, C. L. Giles, and F. M. Coetzee, IEEE Comput. 35, 66 (2002). https://doi.org/10.1109/2.989932 , Google ScholarCrossref
- 20. M. Girvan and M. E. J. Newman, Proc. Natl. Acad. Sci. U.S.A. 99, 7821 (2002). https://doi.org/10.1073/pnas.122653799 , Google ScholarCrossref
- 21. G. Yan, Z. Q. Fu, J. Ren, and W. X. Wang, Phys. Rev. E 75, 016108 (2007). https://doi.org/10.1103/PhysRevE.75.016108 , Google ScholarCrossref
- 22. S. Fortunato, Phys. Rep. 486, 75 (2010). https://doi.org/10.1016/j.physrep.2009.11.002 , Google ScholarCrossref
- 23. L. Danon, A. Arenas, and A. D. Guilera, Phys. Rev. E 77, 036103 (2008). https://doi.org/10.1103/PhysRevE.77.036103 , Google ScholarCrossref
- 24. T. Zhou, M. Zhao, G. Chen, G. Yan, and B. H. Wang, Phys. Lett. A 368, 431 (2007). https://doi.org/10.1016/j.physleta.2007.04.083 , Google ScholarCrossref
- 25. Y. Wu, P. Li, M. Chen, J. Xiao, and J. Kurths, Physica A 388, 2987 (2009). https://doi.org/10.1016/j.physa.2009.03.037 , Google ScholarCrossref
- 26. M. E. J. Newman and M. Girvan, Phys. Rev. E 69, 026113 (2004). https://doi.org/10.1103/PhysRevE.69.026113 , Google ScholarCrossref
- 27. M. E. J. Newman, Phys. Rev. E 69, 066133 (2004). https://doi.org/10.1103/PhysRevE.69.066133 , Google ScholarCrossref
- 28. R. Guimer, M. Sales-Pardo, and L. A. Nunes Amaral, Phys. Rev. E 70, 025101–R (2004). https://doi.org/10.1103/PhysRevE.70.025101 , Google ScholarCrossref
- 29. N. Kashtan and U. Alon, Proc. Natl. Acad. Sci. U.S.A. 102, 13773 (2005). https://doi.org/10.1073/pnas.0503610102 , Google ScholarCrossref
- 30. F. Chung, Spectral Graph Theory (American Mathematical Society, 1997). Google Scholar
- 31. G. Hu, J. Yang, and W. Liu, Phys. Rev. E 58, 4440 (1998). https://doi.org/10.1103/PhysRevE.58.4440 , Google ScholarCrossref
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