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Quantifying social group evolution

Nature volume 446, pages 664–667 (2007)Cite this article

Abstract

The rich set of interactions between individuals in society1,2,3,4,5,6,7 results in complex community structure, capturing highly connected circles of friends, families or professional cliques in a social network3,7,8,9,10. Thanks to frequent changes in the activity and communication patterns of individuals, the associated social and communication network is subject to constant evolution7,11,12,13,14,15,16. Our knowledge of the mechanisms governing the underlying community dynamics is limited, but is essential for a deeper understanding of the development and self-optimization of society as a whole17,18,19,20,21,22. We have developed an algorithm based on clique percolation23,24 that allows us to investigate the time dependence of overlapping communities on a large scale, and thus uncover basic relationships characterizing community evolution. Our focus is on networks capturing the collaboration between scientists and the calls between mobile phone users. We find that large groups persist for longer if they are capable of dynamically altering their membership, suggesting that an ability to change the group composition results in better adaptability. The behaviour of small groups displays the opposite tendency—the condition for stability is that their composition remains unchanged. We also show that knowledge of the time commitment of members to a given community can be used for estimating the community’s lifetime. These findings offer insight into the fundamental differences between the dynamics of small groups and large institutions.

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Figure 1: Structure and schematic dynamics of the two networks considered.
Figure 2: Characteristic features of community evolution.
Figure 3: Evolution of four types of communities in the co-authorship network.
Figure 4: Effects of links between communities.

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References

  1. Watts, D. J. & Strogatz, S. H. Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998)

    Article  ADS  CAS  Google Scholar 

  2. Barabási, A.-L. & Albert, R. Emergence of scaling in random networks. Science 286, 509–512 (1999)

    Article  ADS  MathSciNet  Google Scholar 

  3. Scott, J. Social Network Analysis: A Handbook 2nd edn (Sage Publications, London, 2000)

    Google Scholar 

  4. Dorogovtsev, S. N. & Mendes, J. F. F. Evolution of Networks: From Biological Nets to the Internet and WWW. (Oxford Univ. Press, Oxford, 2003)

    Book  Google Scholar 

  5. Watts, D. J., Dodds, P. S. & Newman, M. E. J. Identity and search in social networks. Science 296, 1302–1305 (2002)

    Article  ADS  CAS  Google Scholar 

  6. Faust, K. in Models and Methods in Social Network Analysis (eds Carrington, P., Scott, J. & Wasserman, S.) Ch. 7 (Cambridge Univ. Press, New York, 2005)

    Google Scholar 

  7. Liljeros, F., Edling, R., Amaral, L. A. N., Stanley, H. E. & Aberg, Y. The web of human sexual contacts. Nature 411, 907–908 (2001)

    Article  ADS  CAS  Google Scholar 

  8. Shiffrin, R. M. & Börner, K. Mapping knowledge domains. Proc. Natl Acad. Sci. USA 101, (suppl. 1)5183–5185 (2004)

    Article  ADS  CAS  Google Scholar 

  9. Newman, M. E. J. Detecting community structure in networks. Eur. Phys. J. B 38, 321–330 (2004)

    Article  ADS  CAS  Google Scholar 

  10. 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)

    Article  ADS  CAS  Google Scholar 

  11. Barabási, A.-L. et al. Evolution of the social network of scientific collaborations. Physica A 311, 590–614 (2002)

    Article  ADS  MathSciNet  Google Scholar 

  12. Holme, P., Edling, R. & Liljeros, F. Structure and time-evolution of an internet dating community. Soc. Networks 26, 155–174 (2004)

    Article  Google Scholar 

  13. Ebel, H., Davidsen, J. & Bornholdt, S. Dynamics of social networks. Complexity 8, 24–27 (2002)

    Article  MathSciNet  Google Scholar 

  14. Wagner, C. S. & Leydesdorff, L. Network structure, self-organization, and the growth of international collaboration in science. Res. Policy 34, 1608–1618 (2005)

    Article  Google Scholar 

  15. Yeung, Y.-Y., Liu, T. C.-Y. & Ng, P.-H. A social network analysis of research collaboration in physics education. Am. J. Phys. 73, 145–150 (2005)

    Article  ADS  Google Scholar 

  16. Newman, M. E. J. & Park, J. Why social networks are different from other types of networks. Phys. Rev. E 68, 036122 (2003)

    Article  ADS  CAS  Google Scholar 

  17. Guimerá, R., Danon, L., Diaz-Guilera, A., Giralt, F. & Arenas, A. Self-similar community structure in organisations. Phys. Rev. E 68, 065103 (2003)

    Article  ADS  Google Scholar 

  18. Hopcroft, J., Khan, O., Kulis, B. & Selman, B. Tracking evolving communities in large linked networks. Proc. Natl Acad. Sci. USA 101, 5249–5253 (2004)

    Article  ADS  CAS  Google Scholar 

  19. Guimerá, R., Uzzi, B., Spiro, J. & Amaral, L. A. N. Team assembly mechanisms determine collaboration network structure and team performance. Science 308, 697–702 (2005)

    Article  ADS  Google Scholar 

  20. Li & Maini, K. An evolving network model with community structure. J. Phys. Math. Gen. 38, 9741–9749 (2005)

    Article  ADS  MathSciNet  Google Scholar 

  21. Pollner, P., Palla, G. & Vicsek, T. Preferential attachment of communities: The same principle, but a higher level. Europhys. Lett. 73, 478–484 (2006)

    Article  ADS  MathSciNet  CAS  Google Scholar 

  22. Kossinets, G. & Watts, D. J. Empirical analysis of an evolving social network. Science 311, 88–90 (2006)

    Article  ADS  MathSciNet  CAS  Google Scholar 

  23. Palla, G., Derényi, I., Farkas, I. & Vicsek, T. Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814–818 (2005)

    Article  ADS  CAS  Google Scholar 

  24. Derényi, I., Palla, G. & Vicsek, T. Clique percolation in random networks. Phys. Rev. Lett. 94, 160202 (2005)

    Article  ADS  Google Scholar 

  25. Warner, S. E-prints and the open archives initiative. Library Hi Tech 21, 151–158 (2003)

    Article  Google Scholar 

  26. Granovetter, M. S. The strength of weak ties. Am. J. Sociol. 78, 1360–1380 (1973)

    Article  Google Scholar 

  27. Csermely, P. Weak Links (Springer, Heidelberg, 2006)

    Google Scholar 

  28. Onnela, J.-P. et al. Structure and tie strengths in mobile communication networks. Preprint at 〈http://lanl.arxiv.org/abs/physics/0610104〉 (2006)

  29. Everett, M. G. & Borgatti, S. P. Analyzing clique overlap. Connections 21, 49–61 (1998)

    Google Scholar 

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Acknowledgements

We thank I. Derényi for suggestions, and G. Szabó and I. Farkas for their assistance with the primary phone-call and co-authorship data sets, respectively. G.P and T.V are supported by OTKA; A.-L.B. is supported by the James S. McDonnell Foundation and the National Science Foundation within the DDDAS and ITR programmes.

Author information

Authors and Affiliations

  1. Statistical and Biological Physics Research Group of the HAS, Pázmány P. stny. 1A, H-1117 Budapest, Hungary,

    Gergely Palla & Tamás Vicsek

  2. Center for Complex Network Research and Departments of Physics and Computer Science, University of Notre Dame, Indiana 46566, USA,

    Albert-László Barabási

  3. Department of Biological Physics, Eötvös University, Pázmány P. stny. 1A, H-1117 Budapest, Hungary,

    Tamás Vicsek

Authors
  1. Gergely Palla
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  2. Albert-László Barabási
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  3. Tamás Vicsek
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Corresponding author

Correspondence to Tamás Vicsek.

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Competing interests

The company that served as the data source for the telephone data offered financial support for consulting and data analysis.

Supplementary information

Supplementary Information

This file contains Supplementary Discussion, Supplementary Figures 1-10 with Legends and additional references. (PDF 542 kb)

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Palla, G., Barabási, AL. & Vicsek, T. Quantifying social group evolution. Nature 446, 664–667 (2007). https://doi.org/10.1038/nature05670

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