The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Heterogeneity in Oscillator Networks: Are Smaller Worlds Easier to Synchronize?
Open accessAbstract
Small-world and scale-free networks are known to be more easily synchronized than regular lattices, which is usually attributed to the smaller network distance between oscillators. Surprisingly, we find that networks with a homogeneous distribution of connectivity are more synchronizable than heterogeneous ones, even though the average network distance is larger. We present numerical computations and analytical estimates on synchronizability of the network in terms of its heterogeneity parameters. Our results suggest that some degree of homogeneity is expected in naturally evolved structures, such as neural networks, where synchronizability is desirable.
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Citations
Cites
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
Classes of small-world networks
Amaral, L. A. N., Scala, A., Barthélémy, M., & Stanley, H. E. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.200327197 · Cited by 3,051
Cortex: Statistics and Geometry of Neuronal Connectivity
Braitenberg, V., & Schüz, A. (1998) · doi:10.1007/978-3-662-03733-1 · Cited by 1,178 · cited 1×
“Most work has dealt with synchronization of oscillators whose topology of interaction has either the SF or SW [2, 17, 18, 19, 20, 21, 22, 23] property, showing that it leads to improved synchronizability when compared to local lattice topology.”
Background
Cited by
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
Complex networks: structure and dynamics
BOCCALETTI, S., LATORA, V, MORENO, Y., CHAVEZ, M., & HWANG, D. (2006) · Physics Reports · doi:10.1016/j.physrep.2005.10.009 · Cited by 11,010
Synchronization in complex networks
Arenas, A., Díaz-Guilera, A., Kurths, J., Moreno, Y., & Zhou, C. (2008) · Physics Reports · doi:10.1016/j.physrep.2008.09.002 · Cited by 3,713
The structure and dynamics of multilayer networks
Boccaletti, S., Bianconi, G., Criado, R., del Genio, C. I., Gómez-Gardeñes, J., Romance, M., Sendiña-Nadal, I., Wang, Z., & Zanin, M. (2014) · Physics Reports · doi:10.1016/j.physrep.2014.07.001 · Cited by 3,125
Bassett, D. S., & Bullmore, E. (2006) · The Neuroscientist · doi:10.1177/1073858406293182 · Cited by 2,600
Critical phenomena in complex networks
Dorogovtsev, S. N., Goltsev, A. V., & Mendes, J. F. F. (2008) · Reviews of Modern Physics · doi:10.1103/revmodphys.80.1275 · Cited by 2,220
Network ‘Small-World-Ness’: A Quantitative Method for Determining Canonical Network Equivalence
Humphries, M. D., & Gurney, K. (2008) · PLoS ONE · doi:10.1371/journal.pone.0002051 · Cited by 1,442
Adaptive reconfiguration of fractal small-world human brain functional networks
Bassett, D. S., Meyer-Lindenberg, A., Achard, S., Duke, T., & Bullmore, E. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0606005103 · Cited by 814
Information cascades in complex networks
Jalili, M., & Perc, M. (2017) · Journal of Complex Networks · doi:10.1093/comnet/cnx019 · Cited by 248
Dopamine-modulated dynamic cell assemblies generated by the GABAergic striatal microcircuit
Humphries, M. D., Wood, R., & Gurney, K. (2009) · Neural Networks · doi:10.1016/j.neunet.2009.07.018 · Cited by 129
Metadata
- Authors
- Nishikawa, T., Motter, A. E., Lai, Y.-C., & Hoppensteadt, F. C.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2003-07-03
- Volume
- 91
- Issue
- 1
- Pages
- 014101-014101
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2082915760
- Cited by
- 839
- References
- 3
- FWCI
- 48.88
- Type
- journal article
- Language
- en
- Open access
- green
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