Stochastic actor-oriented models for network dynamics

Annual Review of Statistics and Its Application

Volumn 4, Issue , 2017, Pages 343-363

  Snijders, Tom A B ^a,b  

^a UNIVERSITY OF GRONINGEN   (Netherlands)

^b UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

Inference; Social networks; Statistical modeling

Indexed keywords

EID: 85015258380     PISSN: 23268298     EISSN: 2326831X     Source Type: Journal    
DOI: 10.1146/annurev-statistics-060116-054035     Document Type: Review

References (70)

1. Amati V, Schönenberger F, Snijders T. 2015. Estimation of stochastic actor-orientedmodels for the evolution of networks by generalized method of moments. J. Soc. Fr. Stat. 156: 140-65
2. Bather J. 1989. Stochastic approximation: A generalisation of the Robbins-Monro procedure. Proc. 4th Prague Symp. Asympt. Stat., ed. P Mandl, M Hu?sková, pp. 13-27. Prague: Charles Univ.
3. Bollobàs B. 1985. Random Graphs. London: Academic Press
4. Brandes U, Robins G, McCranie A, Wasserman S. 2013. What is network science? Netw. Sci. 1: 1-15
5. Burguete J, Gallant AR, Souza G. 1982. On unification of the asymptotic theory of nonlinear econometric models. Econom. Rev. 1: 151-90
6. Durante D, Dunson DB. 2014. Nonparametric Bayes dynamic modelling of relational data. Biometrika 101: 883-98
7. Erd? os P, Rényi A. 1960. On the evolution of random graphs. Mat. Kut. Intézet K?ozleményei 5: 17-61
8. Freeman LC. 2004. The Development of Social Network Analysis: A Study in the Sociology of Science. Vancouver, BC: Empirical Press
9. Goldenberg A, Zheng AX, Fienberg SE, Airoldi EM. 2009. A survey of statistical network models. Found. Trends Mach. Learn. 2: 129-233
10. Graham BS. 2015. Methods of identification in social networks. Annu. Rev. Econ. 7: 465-85
11. Greenan CC. 2015. Diffusion of innovations in dynamic networks. J. R. Stat. Soc. A 178: 147-66
12. Hanneke S, Fu W, Xing EP. 2010. Discrete temporal models for social networks. Electron. J. Stat. 4: 585-605
13. Hansen L. 1982. Large sample properties of generalizedmethod ofmoments estimators. Econometrica 50: 1029-54
14. Hoff PD, Raftery AE, Handcock MS. 2002. Latent space approaches to social network analysis. J. Am. Stat. Assoc. 97: 1090-98
15. Holland PW, Leinhardt S. 1977. A dynamic model for social networks. J. Math. Sociol. 5: 5-20
16. Holme P, Saramäki J. 2012. Temporal networks. Phys. Rep. 519: 97-125
17. HuismanME, SnijdersTAB. 2003. Statistical analysis of longitudinal network data with changing composition. Sociol. Methods Res. 32: 253-87
18. Huitsing G, Snijders TAB, Van Duijn MA, Veenstra R. 2014. Victims, bullies, and their defenders: A longitudinal study of the coevolution of positive and negative networks. Dev. Psychopathol. 26: 645-59
19. Hunter DR. 2007. Curved exponential family models for social networks. Soc. Netw. 29: 216-30
20. Hunter DR, Goodreau SM, Handcock MS. 2008. Goodness of fit of social network models. J. Am. Stat. Assoc. 103: 248-58
21. Jackson MO. 2008. Social and Economic Networks. Princeton, NJ: Princeton Univ. Press
22. Kolaczyk ED. 2009. Statistical Analysis of Network Data: Methods and Models. New York: Springer
23. Koskinen JH, Snijders TAB. 2007. Bayesian inference for dynamic social network data. J. Stat. Plan. Inference 13: 3930-38
24. Koskinen J, Snijders TAB. 2013. Longitudinal models. In Exponential Random Graph Models, ed. D Lusher, J Koskinen, G Robins, pp. 130-40. Cambridge, UK: Cambridge Univ. Press
25. Koskinen JH, Caimo A, Lomi A. 2015. Simultaneous modelling of initial conditions and time heterogeneity in dynamic networks: An application to foreign direct investments. Netw. Sci. 3: 58-77
26. Krivitsky PN, Handcock MS. 2014. A separable model for dynamic networks. J. R. Stat. Soc. B 76: 29-46
27. Kushner HJ, Yin GG. 2003. Stochastic Approximation and Recursive Algorithms and Applications. New York: Springer. 2nd ed.
28. Lerner J, Indlekofer N, Nick B, Brandes U. 2013. Conditional independence in dynamic networks. J. Math. Psychol. 57: 275-83
29. Lospinoso JA. 2012. Statistical Models for Social Network Dynamics. PhD Thesis, Univ. Oxford, Oxford, UK
30. Louis T. 1982. Finding observed information when using the EM algorithm. J. R. Stat. Soc. B 44: 226-33
31. Lusher D, Koskinen J, Robins G. 2013. Exponential Random Graph Models. Cambridge, UK: Cambridge Univ. Press
32. Maddala G. 1983. Limited-Dependent and Qualitative Variables in Econometrics. Cambridge, UK: Cambridge Univ. Press. 3rd ed.
33. Newman M. 2010. Networks: An Introduction. Oxford, UK: Oxford Univ. Press
34. Norris JR. 1997. Markov Chains. Cambridge, UK: Cambridge Univ. Press
35. Orchard T,Woodbury M. 1972. A missing information principle: Theory and applications. Proc. 6th Berkeley Symp. Math. Stat. Probab., Vol. 1, pp. 697-715. Berkeley: Univ. Calif. Press
36. Paul S, O'Malley AJ. 2013. Hierarchical longitudinal models of relationships in social networks. Appl. Stat. 62: 705-22
37. Pearson MA, Michell L. 2000. Smoke rings: Social network analysis of friendship groups, smoking and drugtaking. Drugs: Educ. Prev. Policy 7(1): 21-37
38. Polyak BT. 1990. New method of stochastic approximation type. Autom. Remote Control 51: 937-46
39. Rinaldo A, Fienberg SE, Zhou Y. 2009. On the geometry of discrete exponential families with application to exponential random graph models. Electron. J. Stat. 3: 446-84
40. Ripley RM, Snijders TAB, Bóda Z, Vör ös A, Preciado P. 2016. Manual for Siena version 4.0. Tech. Rep., Univ. Oxford, Dep. Stat., Nuffield College. https: //www.stats.ox.ac.uk/~snijders/siena/RSiena- Manual.pdf
41. Robbins H, Monro S. 1951. A stochastic approximation method. Ann. Math. Stat. 22: 400-7
42. Rubinstein R. 1986. The score function approach for sensitivity analysis of computer simulationmodels. Math. Comput. Simul. 28: 351-79
43. Ruppert D. 1988. Efficient estimation from a slowly convergent Robbins-Monro process. Tech. Rep. 781, Sch. Oper. Res. Ind. Eng., Cornell Univ.
44. Ruppert D. 1991. Stochastic approximation. In Handbook of Sequential Analysis, ed. BK Ghosh, PK Sen, pp. 503-29. New York: Marcel Dekker
45. Schwabe R,Walk H. 1996. On a stochastic approximation procedure based on averaging. Metrika 44: 165-80
46. Schweinberger M. 2011. Instability, sensitivity, and degeneracy of discrete exponential families. J. Am. Stat. Assoc. 106: 1361-70
47. SchweinbergerM, Snijders TAB. 2007. Markov models for digraph panel data: Monte Carlo-based derivative estimation. Comput. Stat. Data Anal. 51(9): 4465-83
48. Snijders TAB. 2001. The statistical evaluation of social network dynamics. Sociol. Methodol. 31: 361-95
49. Snijders TAB. 2011. Statistical models for social networks. Annu. Rev. Sociol. 37: 131-53
50. SnijdersTAB. 2016a. Siena algorithms. Tech. Rep.,Univ. Groningen and Univ.Oxford. https: //www.stats.ox. ac.uk/~snijders/siena/Siena-algorithms.pdf
51. Snijders TAB. 2016b. The multiple flavours of multilevel issues for networks. In Multilevel Network Analysis for the Social Sciences: Theory, Methods and Applications, ed. E Lazega, TAB Snijders, pp. 15-46. New York: Springer
52. Snijders TAB, Doreian P. 2010. Introduction to the special issue on network dynamics. Soc. Netw. 32: 1-3
53. Snijders TAB, Doreian P. 2012. Introduction to the special issue on network dynamics (part 2). Soc. Netw. 34: 289-90
54. Snijders TAB, Koskinen JH, Schweinberger M. 2010a. Maximum likelihood estimation for social network dynamics. Ann. Appl. Stat. 4: 567-88
55. Snijders TAB, Lomi A, Torl ò V. 2013. A model for the multiplex dynamics of two-mode and one-mode networks, with an application to employment preference, friendship, and advice. Soc. Netw. 35: 265-76
56. Snijders TAB, Pattison PE, Robins GL, Handcock MS. 2006. New specifications for exponential random graph models. Sociol. Methodol. 36: 99-153
57. Snijders TAB, Pickup M. 2017. Stochastic actor-oriented models for network dynamics. In Oxford Handbook of Political Networks, ed. JN Victor, M Lubell, AH Montgomery. Oxford: Oxford Univ. Press. In press
58. Snijders TAB, Steglich CEG. 2017a. Actor-Based Models for Analyzing Network Dynamics. Cambridge, UK: Cambridge Univ. Press
59. Snijders TAB, Steglich CEG, eds. 2017b. Social Network Dynamics by Examples. Cambridge, UK: Cambridge Univ. Press
60. Snijders TAB, Steglich CEG, Schweinberger M. 2007. Modeling the co-evolution of networks and behavior. In Longitudinal Models in the Behavioral and Related Sciences, ed. K van Montfort, H Oud, A Satorra, pp. 41-71. Mahwah, NJ: Lawrence Erlbaum
61. Snijders TAB, van de Bunt GG, Steglich C. 2010b. Introduction to actor-based models for network dynamics. Soc. Netw. 32: 44-60
62. Steglich CEG, Snijders TAB, Pearson MA. 2010. Dynamic networks and behavior: Separating selection from influence. Sociol. Methodol. 40: 329-93
63. TannerM,WongW. 1987. The calculation of posterior distributions by data augmentation (with discussion). J. Am. Stat. Assoc. 82: 528-50
64. Tubaro P, Mounier L, Lazega E, Snijders T. 2017. Dynamics of advice-seeking networks among judges at the commercial court of Paris. In Social Network Dynamics by Examples, ed.TSnijders, C Steglich. Cambridge, UK: Cambridge Univ. Press. In press
65. van Duijn MA, Snijders TAB, Zijlstra BH. 2004. p2: A random effects model with covariates for directed graphs. Stat. Neerl. 58: 234-54
66. Veenstra R, Dijkstra JK, Steglich C, Van Zalk MH. 2013. Network-behavior dynamics. J. Res. Adolesc. 23(3): 399-412
67. Wasserman S, Pattison P. 1996. Logit models and logistic regression for social networks: I. An introduction to Markov graphs and p. Psychometrika 61: 401-25
68. Watts DJ, Strogatz SH. 1998. Collective dynamics of 'small-world' networks. Nature 393(6684): 440-42
69. West P, SweetingH. 1996. Background, rationale and design of the West of Scotland 11 to 16 Study.Work. Pap. 52, MRC Med. Sociol. Unit
70. Westveld AH, Hoff PD. 2011. A mixed effects model for longitudinal relational and network data, with applications to international trade and conflict. Ann. Appl. Stat. 5: 843-72