Conditional independence in dynamic networks

Journal of Mathematical Psychology

Volumn 57, Issue 6, 2013, Pages 275-283

  Lerner, Jürgen ^a   Indlekofer, Natalie ^a   Nick, Bobo ^a   Brandes, Ulrik ^a  

^a UNIVERSITY OF KONSTANZ   (Germany)

Author keywords

Conditional independence; ERGM; Network modeling; SAOM

Indexed keywords

EID: 84890157558     PISSN: 00222496     EISSN: 10960880     Source Type: Journal    
DOI: 10.1016/j.jmp.2012.03.002     Document Type: Article

References (22)

1. Brandes, U., Lerner, J., & Snijders, T.A.B. (2009). Networks evolving step by step: statistical analysis of dyadic event data. In Proc. 2009 Intl. Conf. Advances in Social Network Analysis and Mining, ASONAM 2009. (pp. 200-205).
2. Butts C.T. A relational event framework for social action. Sociological Methodology 2008, 38(1):155-200.
3. Cranmer S.J., Desmarais B.A. Inferential network analysis with exponential random graph models. Political Analysis 2011, 19(1):66-86.
4. Crescenzi M.J.C. Reputation and interstate conflict. American Journal of Political Science 2007, 51(2):382-396.
5. Frank O., Strauss D. Markov graphs. Journal of the American Statistical Association 1986, 81(359):832-842.
6. Handcock, M.S., Hunter, D.R., Butts, C.T., Goodreau, S.M., Krivitsky, P.N., & Morris, M. (2011). Ergm: a package to fit, simulate and diagnose exponential-family models for networks. Version 2.4.3. URL. http://statnetproject.org.
7. Handcock, M.S., Hunter, D.R., Butts, C.T., Goodreau, S.M., & Morris, M. (2003). Statnet: software tools for the statistical modeling of network data. Version 2.0. URL. http://statnetproject.org.
8. Hanneke S., Fu W., Xing E.P. Discrete temporal models of social networks. Electronic Journal of Statistics 2010, 4:585-605.
9. Huisman M., Steglich C. Treatment of non-response in longitudinal network studies. Social Networks 2008, 30:297-308.
10. Hunter D.R. Curved exponential family models for social networks. Social Networks 2007, 29:216-230.
11. Hunter D.R., Handcock M.S. Inference in curved exponential family models for networks. Journal of Computational and Graphical Statistics 2006, 15(3):565-583.
12. Knecht, A. (2008). Friendship selection and friends' influence. dynamics of networks and actor attributes in early adolescence. Ph.D. Thesis, University of Utrecht.
13. Maoz Z., Terris L.G., Kuperman R.D., Talmud I. What is the enemy of my enemy? Causes and consequences of imbalanced international relations, 1816-2001. Journal of Politics 2007, 69(1):100-115.
14. Newcomb T. The acquaintance process 1961, Holt, Reinhard & Winston.
15. Ripley, R.M., & Snijders, T.A.B. (2011). Manual for SIENA. Oxford: University of Oxford, Department of Statistics; Nuffield College. URL. http://www.stats.ox.ac.uk/siena/.
16. p *) models for social networks. Social Networks 2007, 29:192-215.
17. Snijders T.A.B. Markov chain Monte Carlo estimation in exponential random graph models. Journal of Social Structure 2002, 3(2).
18. Snijders T.A.B. The statistical evaluation of social network dynamics. Sociological methodology 2001, Basil Blackwell. M.E. Sobel, M.P. Becker (Eds.).
19. Snijders T.A.B. Models for longitudinal network data. Models and methods in social network analysis 2005, Cambridge University Press. P.J. Carrington, J. Scott, S. Wasserman (Eds.).
20. Snijders T.A.B., Koskinen J., Schweinberger M. Maximum likelihood estimation for social network dynamics. Annals of Applied Statistics 2010, 4:567-588.
21. Snijders T.A.B., van de Bunt G.G., Steglich C.E.G. Introduction to actor-based models for network dynamics. Social Networks 2010, 32:44-60.
22. Steglich C.E.G., Snijders T.A.B., Pearson M. Dynamic networks and behavior: separating selection from influence. Sociological Methodology 2010, 40(1):329-393.