2007/04/01 by Gergely Palla, Albert-Ĺaszló Barabási, Albert-Laszlo Barabasi +2 · 8 citations
Mathematics · Physics and Astronomy · Psychology · Social Sciences · #Adaptability #Biology #Clique #Clique percolation method #Community structure #Complex Network Analysis Techniques #Complex network #Computer science #Constant (computer programming) #Data science #Ecology #Evolutionary Game Theory and Cooperation #Group (periodic table) #Machine learning #Mobile phone #Opinion Dynamics and Social Influence #Percolation (cognitive psychology) #Psychology #Set (abstract data type) #Social group #Social media #Social network (sociolinguistics) #Social psychology #Stability (learning theory) #Telecommunications #World Wide Web #physics.soc-ph #stat.AP #stat.ME
paper · pdf · doi:10.1038/nature05670
published as Nature 446, 664 (2007) · 11 pages, 4 figures
openalex publication_date 2007/04/01 · arxiv created 2007/04/05 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
The rich set of interactions between individuals in the society results in complex community structure, capturing highly connected circles of friends, families, or professional cliques in a social network. Thanks to frequent changes in the activity and communication patterns of individuals, the associated social and communication network is subject to constant evolution. Our knowledge of the mechanisms governing the underlying community dynamics is limited, but is essential for a deeper understanding of the development and self-optimisation of the society as a whole. We have developed a new algorithm based on clique percolation, that allows, for the first time, to investigate the time dependence of overlapping communities on a large scale and as such, to uncover basic relationships characterising 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 longer if they are capable of dynamically altering their membership, suggesting that an ability to change the composition results in better adaptability. The behaviour of small groups displays the opposite tendency, the condition for stability being that their composition remains unchanged. We also show that the knowledge of the time commitment of the members to a given community can be used for estimating the community's lifetime. These findings offer a new view on the fundamental differences between the dynamics of small groups and large institutions.