Quantifying social group evolution : Abstract : Nature

Nature 446, 664-667 (5 April 2007) | doi:10.1038/nature05670; Received 2 January 2007; Accepted 8 February 2007

Quantifying social group evolution

Gergely Palla¹, Albert-László Barabási² & Tamás Vicsek^1,³

Statistical and Biological Physics Research Group of the HAS, Pázmány P. stny. 1A, H-1117 Budapest, Hungary
Center for Complex Network Research and Departments of Physics and Computer Science, University of Notre Dame, Indiana 46566, USA
Department of Biological Physics, Eötvös University, Pázmány P. stny. 1A, H-1117 Budapest, Hungary

Correspondence to: Tamás Vicsek^1,³ Correspondence and requests for materials should be addressed to T.V. (Email: vicsek@angel.elte.hu).

The rich set of interactions between individuals in society^1,^2,^3,^4,^5,^6,⁷ results in complex community structure, capturing highly connected circles of friends, families or professional cliques in a social network^3,^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 evolution^7,^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 whole^17,^18,^19,^20,^21,²². We have developed an algorithm based on clique percolation^23,²⁴ 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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