The analysis of social network data: An exciting frontier for statisticians

Statistics in Medicine

Volumn 32, Issue 4, 2013, Pages 539-555

  O'Malley, A James ^a  

^a HARVARD MEDICAL SCHOOL   (United States)

Author keywords

Christakis Fowler; Dyad; Network; Peer effect; Relationship; Social influence; Social selection

Indexed keywords

ARTICLE; DATA ANALYSIS; HEALTH BEHAVIOR; HEALTH STATUS; HUMAN; PEER GROUP; SOCIAL NETWORK; STATISTICAL ANALYSIS;

BIOSTATISTICS; HEALTH BEHAVIOR; HUMANS; MODELS, PSYCHOLOGICAL; MODELS, STATISTICAL; PEER GROUP; SOCIAL SUPPORT;

EID: 84872763319     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.5630     Document Type: Article

References (66)

1. Christakis NA, Fowler JH. Social contagion theory: examining dynamic social networks and human behavior. Statistics in Medicine 2012. DOI: 10:1002/sim.5408.
2. Moreno JL. Who shall survive? A New Approach to the Problem of Human Interrelations, Nervous and mental disease monograph series, no. 58. Nervous and Mental Disease Publishing Co, xvi: Washington, DC, US, 1934.
3. Keating NL, Ayanian JZ, Cleary PD, Marsden PV. Factors affecting influential discussions among physicians: a social network analysis of a primary care practice. Journal of General Internal Medicine 2007; 22:794-798.
4. Pham HH, O'Malley AS, Bach PB, Saiontz-Martinez C, Schrag D. Primary care physicians' links to other physicians through medicare patients: the scope of care coordination. Annals of internal medicine 2009; 150:236-242.
5. Barnett ML, Christakis NA, O'Malley AJ, Onnela J-P, Keating NL, Landon BE. Physician patient-sharing networks and the cost and intensity of care in US hospitals. Medical Care 2012; 50:152-160.
6. Rubin D. Bayesian inference for causal effects: the role of randomization. The Annals of Statistics 1978; 6:34-58.
7. Freeman L. The Development of Social Network Analysis: A Study in the Sociology of Science. Empirical Press: Vancouver, Canada, 2004.
8. O'Malley AJ, Marsden PV. The analysis of social networks. Health Services & Outcomes Research Methodology 2008; 8(4):222-269.
9. Wasserman SS, Faust K. Social Network Analysis: Methods and Applications. Cambridge University Press: Cambridge, United Kingdom, 1994.
10. Christakis NA, Fowler JH. Connected. Little, Brown and Company: New York, 2010.
11. Manski CA. Identification of endogenous social effects: the reflection problem. Review of Economic Studies 1993; 60:531-542.
12. Laumann E, Marsden P, Prensky D. The boundary specification problem in network analysis. In Applied Network Analysis: A Methodological Introduction, Burt R, Minor M (eds). Sage Publication: Beverly Hills, CA, 1983; 18-34.
13. Anselin L. Spatial Econometrics: Methods and Models. Kluwer Academic Publishers: Dordrecht, The Netherlands, 1988.
14. O'Malley AJ, Cotterill P, Schermerhorn ML, Landon BE. Optimal referral strategies involving treatment selection and volume-outcome relationships for aaa repair. Medical Care 2011; 49:1126-1132.
15. Noel H, Nyhan B. The "unfriending" problem: the consequences of homophily in friendship retention for causal estimates of social influence. Social Networks 2011; 33:211-218.
16. VanderWeele TJ. Sensitivity analysis for contagion effects in social networks. Sociological Methods & Research 2011; 40(2):240-255.
17. Fletcher JM. Social interactions and smoking: evidence using multiple student cohorts, instrumental variables, and school fixed effects. Health Economics 2008; 19:466-484.
18. Horton NJ, Laird NM, Zahner GP. Use of multiple informant data as a predictor in psychiatric epidemiology. International Journal of Methods in Psychiatric Research 1999; 8:6-18.
19. VanderWeele TJ, Ogburn EL, TchetgenTchetgen EJ. Why and when "flawed" social network analyses still yield valid tests of no contagion. Statistics, Politics, and Policy 2012; Manuscript 1050.
20. Marsden PV, Friedkin NE. Network studies of social influence. Sociological Methods & Research 1993; 22(1):127-151.
21. Marsden PV, Andrews SB. Network sampling and the network effects model, 1991. Unpublished manuscript, Harvard University.
22. Robins G, Pattison P, Woolcock J. Small and other worlds: global network structures from local processes. American Journal of Sociology 2005; 110:894-936.
23. Erdös P, Rényi A. Random graphs. Publicationes Mathematicae 1959; 6:290-297.
24. Holland P, Leinhardt S. An exponential family of probability-distributions for directed-graph. Journal of American Statistical Association 1981; 76:33-50.
25. Wang W, Wong G. Stochastic blockmodels for directed graphs. Journal of the American Statistical Association 1987; 82:8-19.
26. Fineberg S, Wasserman S. Categorical data analysis of single sociometric relations. In Sociological Methodology. Jossey-Bass: San Francisco, 1981; 156-192.
27. Holland P, Laskey K, Leinhardt S. Stochastic blockmodels: some first steps. Social Networks 1983; 5:109-137.
28. Airoldi EM, Fienberg SE, Joutard CJ, Love TM. Hierarchical Bayesian mixed-membership models and latent pattern discovery. In Frontiers of Statistical Decision Making and Bayesian Analysis: In Honor of James O. Berger, Chen M-H, Dey DK, Müller P, Sun D, Ye K (eds). Springer-Verlag Inc: New York, 2010; 360-375.
29. Choi D, Wolfe P, Airoldi E. Stochastic blockmodels with growing number of classes. Biometrika 2011. In press.
30. Frank O, Strauss D. Markov graphs. Journal of American Statistical Association 1986; 81:832-842.
31. Wasserman S, Pattison P. Logit models and logistic regressions for social networks: I. An introduction to markov graphs and p*. Psychometrika 1996; 61:401-425.
32. Hunter DR, Handcock MS. Inference in curved exponential family models for networks. Journal of Computational and Graphical Statistics 2006; 15:565-583.
33. Snijders TAB. Statistical methods for network dynamics. In Proceedings of the XLIII Scientic Meeting, Italian Statistical Society, Luchini SR, et al. (ed.) Padova: CLEUP, Italy, 2006; 281-296.
34. Strauss D, Ikeda M. Pseudolikelihood estimation for social networks. Journal of American Statistical Association 1990; 85:204-212.
35. VanDuijn MAJ, Gile KJ, Handcock MS. A framework for the comparison of maximum pseudo-likelihood and maximum likelihood estimation of exponential family random graph models. Social Networks 2009; 31(1):52-62.
36. Handcock MS, Robins GL, Snijders TAB, Moody J, Besag J. Assessing degeneracy in statistical models of social networks. Journal of American Statistical Association 2003; 76:33-50.
37. Goodreau S. Advances in exponential random graph (p*) models applied to a large social network. Social Networks 2007; 29:231-248.
38. Robins GL, Snijders TAB, Wang P, Handcock MS, Pattison PE. Recent developments in exponential random graph (p*) models for social networks. Social Networks 2007; 29(2):192-215.
39. Duijn MV, Snijders TAB, Zijlstra B. P2: a random effects model with covariates for directed graphs. Statistica Neerlandica 2004; 58:234-254.
40. Zijlstra BJH, Duijn MV, Snijders TAB. The multilevel p2 model: a random effects model for the analysis of multiple social networks. Methodology 2006; 2:42-47.
41. Nowicki K, Snijders TAB. Estimation and prediction for stochastic blockstructures. Journal of the American Statistical Association 2001; 96(455):1077-1087.
42. Airoldi EM, Fienberg SE, Xing EP. Mixed membership stochastic blockmodels. Journal of Machine Learning Research 2008; 9:1981-2014.
43. Hoff PD, Raftery AE, Handcock MS. Latent space models for social networks analysis. Journal of American Statistical Association 2002; 97:1090-1098.
44. Handcock M, Raftery A, Tantrum J. Model-based clustering for social networks. Journal of the Royal Statistical Society Series A 2007; 170:301-354.
45. Hoff PD. Bilinear mixed effects models for dyadic data. Journal of American Statistical Association 2005; 100:286-295.
46. Hoff P. Modeling homophily and stochastic equivalence in symmetric relational data. In Advances in Neural Information Processing Systems, Vol.20. MIT Press: Cambridge, Massachussets, 2008; 657-664.
47. Raftery A, Niu X, Hoff P, Yeung K. Fast inference for the latent space network model using a case-control approximate likelihood. To appear: Journal of Computational and Graphical Statistics 2012.
48. Paul S, O'Malley AJ. Hierarchical longitudinal models of relationships in social networks. Under Review: Journal of the Royal Statistical Society, Series C 2012.
49. Hanneke S, Wenjie F, Xiang EP. Discrete Temporal models of Social Network. Electronic Journal of Statistics 2010; 4:585-605. DOI: 10.1214/09-EJS548.
50. Hanneke S, Fu W, Xing EP. Discrete temporal models of social networks. Electronic Journal of Statistics 2010; 4:585-605.
51. Krivitsky PN, Handcock MS. A separable model for dynamic networks. arXiv preprint 2010; 1011.1937v1[stat.ME].
52. Snijders T. Stochastic actor-oriented models for network change. Journal of Mathematical Sociology 1996; 21:149-172.
53. Snijders TAB. The statistical evaluation of social network dynamics. In Sociological Methodology. Basil Blackwell: Boston, Massachussets, 2001; 361-395.
54. Snijders TAB. Models for longitudinal social network data. In Models and Methods in Social Network Analysis. Cambridge University Press: Cambridge, United Kingdom, 2005; 215-247.
55. Goldenberg A, Zheng AX, Fieberg SE, Airoldi EM. A survey of statistical network models. Foundations and Trends in Machine Learning 2009; 2:129-233.
56. Huisman M, VanDuijn M. Software for statistical analysis of social networks. The Sixth International Conference on Logic and Methodology, Amsterdam, The Netherlands, 2004.
57. Huisman M, VanDuijn M. Software for social networks analysis. In Models and Methods in Social Network Analysis. Cambridge University Press: Cambridge, United Kingdom, 2005.
58. O'Malley AJ, Christakis NA. Longitudinal analysis of large social networks: estimating the effect of health traits on changes in friendship ties. Statistics in Medicine 2011; 30:950-964.
59. Westveld AH, Hoff PD. A mixed effect model for longitudinal relational and network data, with applications to international trade and conflict. The Annals of Applied Statistics 2011; 5(2A):843-872.
60. Steglich C, Snijders TAB, Pearson M. Dynamic networks and behavior: separating selection from influence. Sociological Methodology 2010; 40:329-393.
61. Szabo G, Barabasi AL. Network effects in service usage. Arxiv preprint 2007. (Available from: http://lanl.arxiv.org/abs/physics/0611177) [Accessed on January 17, 2012].
62. Shalizi CR, Thomas AC. Homophily and contagion are generically confounded in observational social network studies. Sociological Methods & Research 2011; 40(2):211-239.
63. Christakis N, Fowler J. The spread of obesity in a large social network over 32years. New England Journal of Medicine 2007; 357:370-379.
64. Thompson SK. Adaptive web sampling. Biometrics 2006; 62(4):1224-1234.
65. Thompson SK. Targeted random walk designs. Survey Methodology 2006; 32(1):11-24.
66. Gile KJ. Improved inference for respondent-driven sampling data with application to HIV prevalence estimation. Journal of the American Statistical Association 2011; 106(493):135-146.