References
Papers relating to new specifications for exponential random graph models
New specifications
Goodreau, S. (2005). Applying advances in exponential random graph (p*) models to a large social network.
Social Networks. Forthcoming.
Hunter, D. (2005). Curved exponential family models for social networks. Social Networks. Forthcoming.
Hunter, D. & Handcock, M. (2004). Inference in curved exponential family models for networks. Penn State
Department of Statistics Technical Report 0402.
Pattison, P. E., & Robins, G. L. (2002). Neighborhood-based models for social networks. Sociological
Methodology. 32, 301-337.
Robins, G.L., Snijders, T.A.B., & Wang, P. (2005). Recent developments in exponential random graph (p*)
models for social networks. Social Networks. Forthcoming.
Snijders, T.A.B., Pattison, P., Robins, G.L., & Handock, M. (2005). New specifications for exponential
random graph models. Sociological Methodology. (http://stat.gamma.rug.nl/snijders/sprh_f.pdf).
Degeneracy and related issues
Handcock, M.S. (2002). Statistical models for social networks: Degeneracy and inference. In Breiger, R.,
Carley, K., & Pattison, P. (eds.). Dynamic social network modeling and analysis (pp. 229-240).
Washington DC: National Academies Press.
Handcock, M.S. (2003). Assessing degeneracy in statistical models of social networks. Center for Statistics
and the Social Sciences, Working Paper no 39.
Häggström, O., & Jonasson, J. (1999). Phase transition in the random triangle model. Journal of Applied
Probability, 30, 1101-1115.
Park, J., & Newman, M. (2004). Solution of the 2-star model of a network. Condensed Matter abstracts,
cond-mat/0405457.
Robins, G.L., Pattison, P.E., & Woolcock, J. (2005). Social networks and small worlds. American Journal of
Sociology.
Snijders, T.A.B. (2002). Markov chain Monte Carlo estimation of exponential random graph models.
Journal of Social Structure, 3, 2.
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Maximum Likelihood Estimation
Boer, P., Huisman, M., Snijders, T.A.B., & Zeggelink, E. (2003). StOCNET: an open software system for the
advanced analysis of social networks. Groningen: ProGAMMA/ICS.
Corander, J., Dahmström, K., & Dahmström, P. (2002). Maximum likelihood estimation for exponential
random graph models. In J. Hagberg (Ed.), Contributions to social network analysis, information
theory and other topics in statistics: A festschrift in honour of Ove Frank. Stockholm: Department of
Statistics, University of Stockholm.
Crouch, B., & Wasserman, S. (1998). Fitting p*: Monte Carlo maximum likelihood estimation. Paper
presented at International Conference on Social Networks, Sitges, Spain, May 28-31.
Handcock, M., Hunter, D., Butts, C., Goodreau, S., & Morris, M. (2005). Statnet: An R package for the
statistical analysis and simulation of social networks. Working paper: University of Washington.
Hunter, D. & Handcock, M. (2004). Inference in curved exponential family models for networks. Penn State
Department of Statistics Technical Report 0402.
Snijders, T.A.B. (2002). Markov chain Monte Carlo estimation of exponential random graph models.
Journal of Social Structure, 3, 2.
Snijders, T.A.B., & van Duijn, M.A.J. (2002). Conditional maximum likelihood estimation under various
specifications of exponential random graph models. In Jan Hagberg (Ed.), Contributions to social
network analysis, information theory, and other topics: A festschrift in honour of Ove Frank (pp. 117-
134). University of Stockholm: Department of Statistics.
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Other papers related to exponential random graph models
Anderson, C.J., Wasserman, S., & Crouch, B. (1999). A p* primer: Logit models for social networks. Social
Networks, 21, 37-66.
Besag, J. (1974). Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal
Statistical Society, Series B, 36, 96-127.
Contractor, N., Wasserman, S. & Faust, K. (in press). Testing multi-theoretical multilevel hypotheses about
organizational networks: An analytic framework and empirical example. Academy of Management
Journal.
Erdös, P., & Renyi, A. (1959). On random graphs. I. Publicationes Mathematicae (Debrecen), 6, 290-297.
Frank, O. (1981). A survey of statistical methods for graph analysis. Pp. 110-155 in S. Leinhardt (Ed.),
Sociological methodology 1981. San Francisco: Jossey-Bass.
Frank, O. (1991). Statistical analysis of change in networks. Statistica Neerlandica, 95, 283-293.
Frank, O., & Nowicki, K. (1993). Exploratory statistical analysis of networks. In J. Gimbel, J.W. Kennedy &
L.V. Quintas (Eds.), Quo Vadis, Graph Theory? Annals of Discrete Mathematics, 55, 349-366.
Frank, O., & Strauss, D. (1986). Markov graphs. Journal of the American Statistical Association, 81, 832-
842.
Holland, P.W., & Leinhardt, S. (1981). An exponential family of probability distributions for directed graphs
(with discussion). Journal of the American Statistical Association, 76, 33-65.
Koehly, L., & Pattison, P. E. (2005). Random graph models for social networks: multiple relations or multiple
raters. In Carrington, Scott & Wasserman (Eds.), Models and methods in social network analysis. New
York: Cambridge University Press.
Koehly, L., Goodreau, S.M., & Morris, M. (2004). The link between exponential random graph models and
loglinear models for networks. Sociological Methodology, 34, 241-270.
Lazega, E.,& Pattison, P. E. (1999). Multiplexity, generalized exchange and cooperation in organizations.
Social Networks, 21, 67-90.
Lazega, E., & Van Duijn, M. (1997). Position in formal structure, personal characteristics and choices of
advisors in a law firm: A logistic regression model for dyadic network data. Social Networks, 19, 375-
397.
Newman, M. (2003). The structure and function of complex networks. SIAM Review, 45, 167-256.
Pattison, P., & Robins, G.L. (In press). Building models for social space: Neighbourhood based models for
social networks and affiliation structures. Mathematiques des science humaines.
Pattison, P. E., & Wasserman, S. (1999). Logit models and logistic regressions for social networks, II.
Multivariate relations. British Journal of Mathematical and Statistical Psychology, 52, 169-194.
Pattison, P. E., & Wasserman, S. (2002). Multivariate random graph distributions: applications to social
network analysis. J. Hagberg (Ed.), Contributions to social network analysis, information theory and
other topics in statistics: A festschrift in honour of Ove Frank. Stockholm: Department of Statistics,
University of Stockholm.(Pp. 74-100).
Robins, G., Pattison, P., Kalish, Y., & Lusher, D. (2005). A workshop on exponential random graph (p*)
models for social networks. (http ://www.psych.unimelb.edu.au/staff/robins.html)
Robins, G. L., & Pattison, P. E. (2001). Random graph models for temporal processes in social networks.
Journal of Mathematical Sociology, 25, 5-41.
Robins, G. L., & Pattison, P. E. (2005). Interdependencies and social processes: Generalized dependence
structures. In Carrington, Scott & Wasserman (Eds.) Models and Methods in Social Network
Analysis. New York: Cambridge University Press.
Robins, G.L., Elliott, P., & Pattison, P. (2001). Network models for social selection processes. Social
Networks, 23, 1-30.
Robins, G. L., Pattison, P. E., & Elliott, P. (2001). Network models for social influence processes.
Psychometrika, 66, 161-190.
Robins, G. L., Pattison, P. E., & Wasserman, S. (1999). Logit models and logistic regressions for social
networks, III. Valued relations. Psychometrika, 64, 371-394.
Robins, G.L., Pattison, P., & Woolcock, J. (2004). Models for social networks with missing data. Social
Networks, 26, 257-283.
Schweinberger M, & Snijders T. (2003). Settings in social networks: A measurement model. Sociological
Methodology.,33, 307-341.
Skvoretz, J., & Faust, K. (1999). Logit models for affiliation networks. In Michael Sobel and Mark Becker
(Eds.), Sociological Methodology 1999 (pp. 253-280). New York: Blackwell.
Snijders, T.A.B. (2001). The statistical evaluation of social network dynamics. In M.E. Sobel and M.P. Becker
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Van Duijn, M. A. J., Snijders, T.A.B., & Zijlstra, B. J. H. (2004). p2: a random effects model with covariates
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Wasserman, S., & Pattison, P. E. (1996). Logit models and logistic regressions for social networks: I. An
introduction to Markov graphs and p*. Psychometrika, 61, 401-425.
Wasserman, S., & Robins, G. (2005). An Introduction to Random Graphs, Dependence Graphs, and p*. In
Carrington, Scott & Wasserman (Eds.) Models and Methods in Social Network Analysis. New York:
Cambridge University Press
The information on this page was contributed by Dr Garry Robins
