The organization of strong links in complex networks

Nature Physics

Volumn 8, Issue 5, 2012, Pages 429-436

  Pajevic, Sinisa ^a   Plenz, Dietmar ^b  

^a NATIONAL INSTITUTES OF HEALTH   (United States)

^b NATIONAL INSTITUTE OF MENTAL HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84860430806     PISSN: 17452473     EISSN: 17452481     Source Type: Journal    
DOI: 10.1038/nphys2257     Document Type: Article

References (41)

1. Watts, D. J. & Strogatz, S. H. Collective dynamics of 'small-world' networks. Nature 393, 440-442 (1998).
2. Amaral, L. A., Scala, A., Barthelemy, M. & Stanley, H. E. Classes of small-world networks. Proc. Natl Acad. Sci. USA 97, 11149-11152 (2000).
3. Dorogovtsev, S. N. & Mendes, J. F. F. Evolution of Networks: From Biological Nets to the Internet and WWW (University Press, 2003).
4. Strogatz, S. H. Exploring complex networks. Nature 410, 268-276 (2001).
5. Latora, V. & Marchiori, M. Efficient behavior of small-world networks. Phys. Rev. Lett. 87, 198701 (2001).
6. Petermann, T. & De Los, R. P. Physical realizability of small-world networks. Phys. Rev. E 73, 026114 (2006).
7. Achard, S. & Bullmore, E. Efficiency and cost of economical brain functional networks. PLoS Comput. Biol. 3, e17 (2007).
8. Yu, S., Huang, D., Singer, W. & Nikolic, D. A small world of neuronal synchrony. Cereb. Cortex 18, 2891-2901 (2008).
9. Hagmann, P. et al. Mapping the structural core of human cerebral cortex. PLoS. Biol. 6, e159 (2008).
10. Sporns, O. & Honey, C. J. Small worlds inside big brains. Proc. Natl Acad. Sci. USA 103, 19219-19220 (2006).
11. Sporns, O. Small-world connectivity, motif composition, and complexity of fractal neuronal connections. Biosystems 85, 55-64 (2006).
12. Eguiluz, V. M., Chialvo, D. R., Cecchi, G. A., Baliki, M. & Apkarian, A. V. Scale-free brain functional networks. Phys. Rev. Lett. 94, 018102 (2005).
13. Bassett, D. S., Meyer-Lindenberg, A., Achard, S., Duke, T. & Bullmore, E. Adaptive reconfiguration of fractal small-world human brain functional networks. Proc. Natl Acad. Sci. USA 103, 19518-19523 (2006).
14. Beggs, J. M. & Plenz, D. Neuronal avalanches in neocortical circuits. J. Neurosci. 23, 11167-11177 (2003).
15. Petermann, T. et al. Spontaneous cortical activity in awake monkeys composed of neuronal avalanches. Proc. Natl Acad. Sci. USA 106, 15921-15926 (2009).
16. Pajevic, S. & Plenz, D. Efficient network reconstruction from dynamical cascades identifies small-world topology from neuronal avalanches. PLoS Comput. Biol. 5, e1000271 (2008).
17. Barrat, A., Barthelemy, M. & Vespignani, A. Weighted evolving networks: Coupling topology and weight dynamics. Phys. Rev. Lett. 92, 228701 (2004).
18. Barrat, A., Barthelemy, M., Pastor-Satorras, R. & Vespignani, A. The architecture of complex weighted networks. Proc. Natl Acad. Sci. USA 101, 3747-3752 (2004).
19. Bianconi, G. Emergence of weight-topology correlations in complex scale-free networks. Europhys. Lett. 71, 1029-1035 (2005).
20. Serrano, M. A., Boguna, M. & Pastor-Satorras, R. Correlations in weighted networks. Phys. Rev. E 74, 055101 (2006).
21. Restrepo, J. G., Ott, E. & Hunt, B. R. Weighted percolation on directed networks. Phys. Rev. Lett. 100, 058701 (2008).
22. Restrepo, J. G., Ott, E. & Hunt, B. R. Characterizing the dynamical importance of network nodes and links. Phys. Rev. Lett. 97, 094102 (2006).
23. Csermely, P. Weak Links: The Universal Key to the Stability of Networks and Complex Systems (Springer, 2006).
24. Granovetter, M. S. The strength of weak ties. Am. J. Soc. 78, 1360-1380 (1973).
25. Davis, J. Clustering and structural balance in graphs. Hum. Relations 20, 181-187 (1967).
26. Onnela, J. P. et al. Structure and tie strengths in mobile communication networks. Proc. Natl Acad. Sci. USA 104, 7332-7336 (2007).
27. Radicchi, F., Castellano, C., Cecconi, F., Loreto, V. & Parisi, D. Defining and identifying communities in networks. Proc. Natl Acad. Sci. USA 101, 2658-2663 (2004).
28. Serrano, M. A. & Boguna, M. Clustering in complex networks. I. General formalism. Phys. Rev. E 74, 056114 (2006).
29. Gregoretti, F., Belcastro, V., di Bernardo, D. & Oliva, G. A parallel implementation of the network identification by multiple regression (NIR) algorithm to reverse-engineer regulatory gene networks. PLoS ONE 5, e10179 (2010).
30. Albert, R., Jeong, H. & Barabasi, A. L. Error and attack tolerance of complex networks. Nature 406, 378-382 (2000).
31. Garas, A., Argyrakis, P. & Shavlin, S. The structural role of weak and strong links in a financial market network. Eur. Phys. J. B. 63, 265-271 (2008).
32. Ozik, J., Hunt, B. R. & Ott, E. Growing networks with geographical attachment preference: Emergence of small worlds. Phys. Rev. E 69, 026108 (2004).
33. Newman, M. E. & Watts, D. J. Scaling and percolation in the small-world network model. Phys. Rev. E 60, 7332-7342 (1999).
34. Barrat, A., Barthelemy, M. & Vespignani, A. Modeling the evolution of weighted networks. Phys. Rev. E 70, 066149 (2004).
35. Yook, S. H., Jeong, H., Barabasi, A. L. & Tu, Y. Weighted evolving networks. Phys. Rev. Lett. 86, 5835-5838 (2001).
36. Kumpula, J. M., Onnela, J. P., Saramaki, J., Kaski, K. & Kertesz, J. Emergence of communities in weighted networks. Phys. Rev. Lett. 99, 228701 (2007).
37. Wang, S., Szalay, M. S., Zhang, C. & Csermely, P. Learning and innovative elements of strategy adoption rules expand cooperative network topologies. PLoS ONE 3, e1917 (2008).
38. Girvan, M. & Newman, M. E. Community structure in social and biological networks. Proc. Natl Acad. Sci. USA 99, 7821-7826 (2002).
39. Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction (MIT Press, 1998).
40. Schultz, W., Dayan, P. & Montague, P. R. A neural substrate of prediction and reward. Science 275, 1593-1599 (1997).
41. Maslov, S. & Sneppen, K. Specificity and stability in topology of protein networks. Science 296, 910-913 (2002).