Quantifying social group evolution Export

@article{citeulike:1206611, 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.}, author = {Palla, Gergely and Barabasi, Albert-Laszlo and Vicsek, Tamas}, citeulike-article-id = {1206611}, citeulike-linkout-0 = {http://dx.doi.org/10.1038/nature05670}, citeulike-linkout-1 = {http://dx.doi.org/10.1038/nature05670}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/17410175}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=17410175}, citeulike-linkout-4 = {http://arxiv.org/pdf/0704.0744}, day = {05}, doi = {10.1038/nature05670}, issn = {0028-0836}, journal = {Nature}, keywords = {07, auto-correlation, cell, collaboration, community, complex\_network, complexity, cooperation, cultural-evolution, dynamics, evolution, generations, group, k-clique, network, networks, networks\_dynamic, networktheory, percolation, phone, quantifying, quantitative, scientific, social, social-dynamics, social-network, social-networks, social-structure, social\_analysis, social\_network, social\_networks, socialnetworking, sociology, sociophysics, swarm, tagging, taggingpres}, month = {April}, number = {7136}, pages = {664--667}, posted-at = {2007-04-19 20:43:41}, priority = {2}, publisher = {Nature Publishing Group}, title = {Quantifying social group evolution}, url = {http://dx.doi.org/10.1038/nature05670}, volume = {446}, year = {2007} }

TY - JOUR ID - citeulike:1206611 L3 - citeulike-article-id:1206611 N2 - 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. IS - 7136 JF - Nature SN - 0028-0836 EP - 667 TI - Quantifying social group evolution PB - Nature Publishing Group VL - 446 SP - 664 KW - 07 KW - auto-correlation KW - cell KW - collaboration KW - community KW - complex_network KW - complexity KW - cooperation KW - cultural-evolution KW - dynamics KW - evolution KW - generations KW - group KW - k-clique KW - network KW - networks KW - networks_dynamic KW - networktheory KW - percolation KW - phone KW - quantifying KW - quantitative KW - scientific KW - social KW - social-dynamics KW - social-network KW - social-networks KW - social-structure KW - social_analysis KW - social_network KW - social_networks KW - socialnetworking KW - sociology KW - sociophysics KW - swarm KW - tagging KW - taggingpres AU - Palla, Gergely AU - Barabasi, Albert-Laszlo AU - Vicsek, Tamas PY - 2007/04/05/ UR - http://dx.doi.org/10.1038/nature05670 ER -