Random graph models of social networks Export

@article{citeulike:691419, abstract = {10.1073/pnas.012582999 We describe some new exactly solvable models of the structure of social networks, based on random graphs with arbitrary degree distributions. We give models both for simple unipartite networks, such as acquaintance networks, and bipartite networks, such as affiliation networks. We compare the predictions of our models to data for a number of real-world social networks and find that in some cases, the models are in remarkable agreement with the data, whereas in others the agreement is poorer, perhaps indicating the presence of additional social structure in the network that is not captured by the random graph.}, address = {Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA. mark@santafe.edu}, author = {Newman, M. E. J. and Watts, D. J. and Strogatz, S. H.}, citeulike-article-id = {691419}, citeulike-linkout-0 = {http://dx.doi.org/10.1073/pnas.012582999}, citeulike-linkout-1 = {http://www.pnas.org/content/99/suppl_1/2566.abstract}, citeulike-linkout-2 = {http://www.pnas.org/content/99/suppl_1/2566.full.pdf}, citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/11875211}, citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=11875211}, day = {19}, doi = {10.1073/pnas.012582999}, issn = {0027-8424}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, keywords = {social-networks}, month = {February}, number = {Suppl 1}, pages = {2566--2572}, posted-at = {2009-10-29 09:05:57}, priority = {2}, title = {Random graph models of social networks}, url = {http://dx.doi.org/10.1073/pnas.012582999}, volume = {99}, year = {2002} }

TY - JOUR ID - citeulike:691419 L3 - citeulike-article-id:691419 N2 - 10.1073/pnas.012582999 We describe some new exactly solvable models of the structure of social networks, based on random graphs with arbitrary degree distributions. We give models both for simple unipartite networks, such as acquaintance networks, and bipartite networks, such as affiliation networks. We compare the predictions of our models to data for a number of real-world social networks and find that in some cases, the models are in remarkable agreement with the data, whereas in others the agreement is poorer, perhaps indicating the presence of additional social structure in the network that is not captured by the random graph. IS - Suppl 1 JF - Proceedings of the National Academy of Sciences of the United States of America SN - 0027-8424 AD - Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA. mark@santafe.edu EP - 2572 TI - Random graph models of social networks VL - 99 SP - 2566 KW - social-networks AU - Newman, MEJ AU - Watts, DJ AU - Strogatz, SH PY - 2002/02/19/ UR - http://dx.doi.org/10.1073/pnas.012582999 ER -