Skip to main content Accessibility help
Home
Hostname: page-component-ffbbcc459-2vc98 Total loading time: 0.2 Render date: 2022-03-25T11:10:10.831Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "useNewApi": true }

Friendship networks and social status

Published online by Cambridge University Press:  15 April 2013

Abstract

HTML view is not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

In empirical studies of friendship networks, participants are typically asked, in interviews or questionnaires, to identify some or all of their close friends, resulting in a directed network in which friendships can, and often do, run in only one direction between a pair of individuals. Here we analyze a large collection of such networks representing friendships among students at US high and junior-high schools and show that the pattern of unreciprocated friendships is far from random. In every network, without exception, we find that there exists a ranking of participants, from low to high, such that almost all unreciprocated friendships consist of a lower ranked individual claiming friendship with a higher ranked one. We present a maximum-likelihood method for deducing such rankings from observed network data and conjecture that the rankings produced reflect a measure of social status. We note in particular that reciprocated and unreciprocated friendships obey different statistics, suggesting different formation processes, and that rankings are correlated with other characteristics of the participants that are traditionally associated with status, such as age and overall popularity as measured by total number of friends.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2013

References

Airoldi, Edoardo M., Choi, David S., & Wolfe, Patrick J. (2011). Confidence sets for network structure. Statistical Analysis and Data Mining, 4, 461469.CrossRefGoogle Scholar
Ali, I., Cook, W. D., & Kress, M. (1986). On the minimum violations ranking of a tournament. Management Science, 32, 660672.CrossRefGoogle Scholar
Anderson, C., Srivastava, S., Beer, Jennifer S., Spataro, Sandra E., & Chatman, Jennifer A. (2006). Knowing your place: Self-perceptions of status in face-to-face groups. Journal of Personality and Social Psychology, 91, 10941110.CrossRefGoogle ScholarPubMed
Callaghan, T., Mucha, P. J., & Porter, M. A. (2004). The bowl championship series: A mathematical review. Notices of the American Mathematical Society, 51, 887893.Google Scholar
Coleman, James S. (1961). The Adolescent Society: The Social Life of the Teenager and Its Impact on Education. Westport, CT: Greenwood Press.Google Scholar
Davis, James A., & Leinhardt, S. (1972). The structure of positive interpersonal relations in small groups. Sociological Theories in Progress, 2, 218251.Google Scholar
De Vries, H. (1998). Finding a dominance order most consistent with a linear hierarchy: A new procedure and review. Animal Behaviour, 55, 827843.CrossRefGoogle ScholarPubMed
Dijkstra, J. K., Cillessen, Antonius H. N., Lindenberg, S., & Veenstra, R. (2010). Basking in reflected glory and its limits: Why adolescents hang out with popular peers. Journal of Research on Adolescents, 20, 942958.CrossRefGoogle Scholar
Doreian, P., Batagelj, V., & Ferligoj, A. (2000). Symmetric-acyclic decompositions of networks. Journal of Classification, 17, 328.CrossRefGoogle Scholar
Drews, C. (1993). The concept and definition of dominance in animal behaviour. Behaviour, 125, 283313.CrossRefGoogle Scholar
Goldenberg, A., Zheng, Alice X., Feinberg, Stephen E., & Airoldi, Edoardo M. (2009). A survey of statistical network structures. Foundations and Trends in Machine Learning, 2, 1117.CrossRefGoogle Scholar
Hallinan, Maureen T., & Kubitschek, Warren N. (1988). The effect of individual and structural characteristics on intransitivity in social networks. Social Psychology Quarterly, 51, 8192.CrossRefGoogle Scholar
Holland, P. W., & Leinhardt, S. (1981). An exponential family of probability distributions for directed graphs. Journal of the American Statistical Association, 76, 3350.CrossRefGoogle Scholar
Homans, G. C. (1950). The Human Group. San Diego, CA: Harcourt Brace.Google Scholar
Karrer, B., & Newman, M. E. J. (2011). Stochastic blockmodels and community structure in networks. Physical Review E, 83, 016107.Google ScholarPubMed
Rapoport, A., & Horvath, W. J. (1961). A study of a large sociogram. Behavioral Science, 6, 279291.CrossRefGoogle ScholarPubMed
Reinelt, G. (1985). The Linear Ordering Problem: Algorithms and Applications. Berlin, Germany: Heldermann.Google Scholar
Snijders, Tom A. B. (2011). Statistical models for social networks. Annual Review of Sociology, 37, 131153.CrossRefGoogle Scholar
Sørensen, Aage B., & Hallinan, Maureen T. (1976). A stochastic model for change in group structure. Social Science Research, 5, 4361.CrossRefGoogle Scholar
Stefani, R. (1997). Survey of the major world sports rating systems. Journal of Applied Statistics, 24, 635646.CrossRefGoogle Scholar
Strauss, D., & Ikeda, M. (1990). Pseudolikelihood estimation for social networks. Journal of the American Statistical Association, 85, 204212.CrossRefGoogle Scholar
Wang, Yuchung J., & Wong, George Y. (1987). Stochastic blockmodels for directed graphs. Journal of the American Statistical Association, 82, 819.CrossRefGoogle Scholar
Wasserman, S., & Faust, K. (1994). Social Network Analysis. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Wasserman, S., & Pattison, P. (1996). Logit models and logistic regressions for social networks: I. An introduction to Markov random graphs and p*. Psychometrika, 61, 401426.CrossRefGoogle Scholar
You have Access