Large-network concepts and small-network characteristics: Fixed and variable factors

Sexually Transmitted Diseases

Volumn 34, Issue 8, 2007, Pages 604-612

  Rothenberg, Richard ^a   Muth, Stephen Q ^b  

^a EMORY UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b Quintus ential Solutions   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; EMPIRICISM; EPIDEMIOLOGICAL DATA; HUMAN IMMUNODEFICIENCY VIRUS INFECTION; SEXUALLY TRANSMITTED DISEASE; THEORETICAL SAMPLE; VIRUS TRANSMISSION;

CLUSTER ANALYSIS; CONTACT TRACING; DISEASE TRANSMISSION, HORIZONTAL; HIV INFECTIONS; HUMANS; INTERPERSONAL RELATIONS; SEXUALLY TRANSMITTED DISEASES; SOCIAL SUPPORT;

EID: 34547641912     PISSN: 01485717     EISSN: None     Source Type: Journal    
DOI: 10.1097/01.olq.0000258358.13825.a8     Document Type: Article

References (29)

1. Freeman LC. Some antecedents of social network analysis. Connections 1996; 19:39-42.
2. Moreno JL. Who Shall Survive? Foundations of Sociometry, Group Psychotherapy, and Sociodrama. Washington, DC: Nervous and Mental Disease Publishing; 1934.
3. Pool IDS, Kochen M. Contacts and influence. Soc Networks 1978; 1:5-51.
4. Erdos P, Renyi A. On the evolution of random graphs. Publications of the mathematical institute of the Hungarian Academy of Sciences 1960; 5:17-61.
5. Frank O. A survey of statistical methods for graph analysis. In: Leinhardt SL, ed. Sociological Methodology. San Francisco, CA: Jossey-Bass; 1981.
6. Rapoport A. A probabilistic approach to networks. Soc Networks 1979; 2:1-18.
7. Granovetter MS. The strength of weak ties. Am J Sociol 1973; 78:1360-1380.
8. Moschovitis CJP, Poole H, Schuyler T, et al. History of the Internet. The Moschovitis Group. 1999. Available at: http://www.historyoftheinternet.com/ index.html.
9. Lyman P, Varian HR. How much information 2003. School of Information and System Management, University of California at Berkeley. Available at: http://www.sims.berkeley.edu/research/projects/how-much-info-2003/. Accessed October 27, 2003.
10. Newman MEJ. The structure and function of complex networks. SIAM Rev 2003; 45:167-256.
11. Morris M. Local rules and global properties: Modeling the emergence of network structure. In: Breiger R, Carley K, Pattison P, eds. Dynamic Social Network Modeling and Analysis. Washington, DC: National Academy Press; 2003.
12. Barabasi AL, Albert R. Emergence of scaling in random networks. Science 1999; 286:509-512.
13. Pastor-Satorras R, Vespignani A. Epidemic spreading in scale-free networks. Phys Rev Lett 2002; 86:3200-3203.
14. Snijders TAB, Pattison P, Robins G, et al. New specifications for exponential random graph models. Center for Statistics in the Social Sciences, University of Washington, Seattle, WA. Available at: http://www.csss.washington. edu/Papers/wp42.pdf. Accessed April 23, 2004.
15. Newman MEJ. Power laws, Pareto distributions and Zipf's law. Available at: http://www.arXiv.org arXiv:cond-mat/0412004 v2. Accessed January 9, 2005.
16. Borgatti SP, Everett MG, Freeman LC. UCINET for Windows: Software for Social Network Analysis. Natick, MA: Analytic Technologies; 2002.
17. Watts CH, May RM. The influence of concurrent partnerships on the dynamics of HIV/AIDS. Math Biosci 1992; 108:89-104.
18. Kretzschmar M, Morris M. Measures of concurrency in networks and the spread of infectious disease. Math Biosci 1996; 133:165-195.
19. Morris M, Kretzschmar M. Concurrent partnerships and transmission dynamics in networks. Soc Networks 1995; 17:299-318.
20. Morris M, Kretzschmar M. Concurrent partnerships and the spread of HIV. AIDS 1997; 11:641-648.
21. Newman MEJ. Assortative mixing in networks. arXiv:cond-mat/0205405 v1. Available at: http://www.arXiv.org. Accessed May 20, 2002.
22. Morris M. A log-linear modeling framework for selective mixing. Math Biosci 1991; 107:349-377.
23. Rothenberg RB, Sterk C, Toomey KE, et al. Using social network and ethnographic tools to evaluate syphilis transmission. Sex Transm Dis 1998; 25:154-160.
24. Jones JH, Handcock MS. An assessment of preferential attachment as a mechanism for human sexual network formation. Proc R Soc London B 2002; 270:1123-1128.
25. Morris M. Telling tails explain the discrepancy in sexual partner reports. Nature 1994; 365:437-440.
26. Schneeberger A. Scale-free Networks and Sexually Transmitted Disease: A Description of Observed Patterns of Sexual Contacts in Britain and Zimbabwe. IMA Workshop 3: Networks and the Population Dynamics of Disease Transmission November 17-21, 2003. Minneapolis, MN; 2003. Available at: http://www.ima.umn. edu/complex/abstracts/11-17abs.html#schneeberger.
27. Liljeros F, Edling CR, Stanley HE, et al. Distributions of number of sexual partnerships have power law decaying tails and finite variance. arXiv.org e-Print archive. 2003. Available at: http://www.arxiv.org/abs/cond-mat/0305528.
28. Newman MEJ, Strogatz SH, Watts DJ. Random graphs with arbitrary degree distributions and their applications. Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics 2001; 64:026118.
29. Rothenberg RB. Transmission of HIV and STDs: An hypothesis for the interaction of risk, network configuration and geography. Sex Transm Infect, in press.