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Welcome to the first version of ChiSocNet’s Summer School! This website was created for the ERGMs component of the school. Here, you will find three subpages with the following content: (1) Fundamentals about R and Statistics, (2) Introduction to ERGMs, (3) Advanced ERGMs, and (4) New topics in Network Modeling.

1 References

Although not all about ERGMs, the following list of references is an excellent resource for statistical inference in network science.

“1.1: Basic Definitions and Concepts.” 2014. Statistics LibreTexts. https://stats.libretexts.org/Bookshelves/Introductory_Statistics/Introductory_Statistics_(Shafer_and_Zhang)/01%3A_Introduction_to_Statistics/1.01%3A_Basic_Definitions_and_Concepts.

Barrett, Tyson, Matt Dowle, and Arun Srinivasan. 2023. Data.table: Extension of ‘Data.frame‘. https://r-datatable.com.

Bell, Brooke M., Donna Spruijt-Metz, George G. Vega Yon, Abu S. Mondol, Ridwan Alam, Meiyi Ma, Ifat Emi, John Lach, John A. Stankovic, and Kayla De La Haye. 2019. “Sensing Eating Mimicry Among Family Members.” Translational Behavioral Medicine. https://doi.org/10.1093/tbm/ibz051.

Brandenberger, Laurence. 2020. “Interdependencies in Conflict Dynamics: Analyzing Endogenous Patterns in Conflict Event Data Using Relational Event Models.” In Computational Conflict Research, edited by Emanuel Deutschmann, Jan Lorenz, Luis G. Nardin, Davide Natalini, and Adalbert F. X. Wilhelm, 67–80. Computational Social Sciences. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-29333-8_4.

Brooks, S., A. Gelman, G. Jones, and X. L. Meng. 2011. Handbook of Markov Chain Monte Carlo. CRC Press.

Butts, Carter T., Alessandro Lomi, Tom A. B. Snijders, and Christoph Stadtfeld. 2023. “Relational Event Models in Network Science.” Network Science 11 (2): 175–83. https://doi.org/10.1017/nws.2023.9.

Casella, George, and Roger L. Berger. 2021. Statistical Inference. Cengage Learning.

Fellows, Ian E. 2012. “Exponential Family Random Network Models.” ProQuest Dissertations and Theses. PhD thesis. https://login.ezproxy.lib.utah.edu/login?url=https://www.proquest.com/dissertations-theses/exponential-family-random-network-models/docview/1221548720/se-2.

Frank, O, and David Strauss. 1986. “Markov graphs.” Journal of the American Statistical Association 81 (395): 832–42. https://doi.org/10.2307/2289017.

Gelman, Andrew. 2018. “The Failure of Null Hypothesis Significance Testing When Studying Incremental Changes, and What to Do About It.” Personality and Social Psychology Bulletin 44 (1): 16–23. https://doi.org/10.1177/0146167217729162.

Geyer, Charles J., and Elizabeth A. Thompson. 1992. “Constrained Monte Carlo Maximum Likelihood for Dependent Data.” Journal of the Royal Statistical Society. Series B (Methodological) 54 (3): 657–99. https://www.jstor.org/stable/2345852.

Greenland, Sander, Stephen J. Senn, Kenneth J. Rothman, John B. Carlin, Charles Poole, Steven N. Goodman, and Douglas G. Altman. 2016. “Statistical Tests, P Values, Confidence Intervals, and Power: A Guide to Misinterpretations.” European Journal of Epidemiology 31 (4): 337–50. https://doi.org/10.1007/s10654-016-0149-3.

Handcock, Mark S., David R. Hunter, Carter T. Butts, Steven M. Goodreau, Pavel N. Krivitsky, and Martina Morris. 2023. Ergm: Fit, Simulate and Diagnose Exponential-Family Models for Networks. The Statnet Project (https://statnet.org). https://CRAN.R-project.org/package=ergm.

Haye, Kayla de la, Heesung Shin, George G. Vega Yon, and Thomas W. Valente. 2019. “Smoking Diffusion Through Networks of Diverse, Urban American Adolescents over the High School Period.” Journal of Health and Social Behavior. https://doi.org/10.1177/0022146519870521.

Holland, Paul W., and Samuel Leinhardt. 1981. “An exponential family of probability distributions for directed graphs.” Journal of the American Statistical Association 76 (373): 33–50. https://doi.org/10.2307/2287037.

Hunter, David R. 2007. “Curved Exponential Family Models for Social Networks.” Social Networks 29 (2): 216–30. https://doi.org/10.1016/j.socnet.2006.08.005.

Hunter, David R., Mark S. Handcock, Carter T. Butts, Steven M. Goodreau, and Martina Morris. 2008. “ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks.” Journal of Statistical Software 24 (3): 1–29. https://doi.org/10.18637/jss.v024.i03.

Hunter, David R., Pavel N. Krivitsky, and Michael Schweinberger. 2012. “Computational Statistical Methods for Social Network Models.” Journal of Computational and Graphical Statistics 21 (4): 856–82. https://doi.org/10.1080/10618600.2012.732921.

Karrer, Brian, and M. E. J. Newman. 2011. “Stochastic Blockmodels and Community Structure in Networks.” Physical Review E 83 (1): 016107. https://doi.org/10.1103/PhysRevE.83.016107.

Koskinen, Johan, and Galina Daraganova. 2022. “Bayesian Analysis of Social Influence.” Journal of the Royal Statistical Society Series A: Statistics in Society 185 (4): 1855–81. https://doi.org/10.1111/rssa.12844.

Koskinen, Johan, Pete Jones, Darkhan Medeuov, Artem Antonyuk, Kseniia Puzyreva, and Nikita Basov. 2023. “Analysing Networks of Networks.” Social Networks 74 (July): 102–17. https://doi.org/10.1016/j.socnet.2023.02.002.

Koskinen, Johan, Peng Wang, Garry Robins, and Philippa Pattison. 2018. “Outliers and Influential Observations in Exponential Random Graph Models.” Psychometrika 83 (4): 809–30. https://doi.org/10.1007/s11336-018-9635-8.

Krivitsky, Pavel N. 2012. “Exponential-Family Random Graph Models for Valued Networks.” Electronic Journal of Statistics 6: 1100–1128. https://doi.org/10.1214/12-EJS696.

———. 2017. “Using Contrastive Divergence to Seed Monte Carlo MLE for Exponential-Family Random Graph Models.” Computational Statistics & Data Analysis 107 (March): 149–61. https://doi.org/10.1016/j.csda.2016.10.015.

———. 2023. Ergm.multi: Fit, Simulate and Diagnose Exponential-Family Models for Multiple or Multilayer Networks. The Statnet Project (https://statnet.org). https://CRAN.R-project.org/package=ergm.multi.

Krivitsky, Pavel N., Pietro Coletti, and Niel Hens. 2023a. “A Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks.” Journal of the American Statistical Association 0 (0): 1–21. https://doi.org/10.1080/01621459.2023.2242627.

———. 2023b. “Rejoinder to Discussion of ‘A Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks’.” Journal of the American Statistical Association 118 (544): 2235–38. https://doi.org/10.1080/01621459.2023.2280383.

Krivitsky, Pavel N., David R. Hunter, Martina Morris, and Chad Klumb. 2023. “Ergm 4: New Features for Analyzing Exponential-Family Random Graph Models.” Journal of Statistical Software 105 (January): 1–44. https://doi.org/10.18637/jss.v105.i06.

Leifeld, Philip. 2013. “Texreg : Conversion of Statistical Model Output in R to L A T E X and HTML Tables.” Journal of Statistical Software 55 (8). https://doi.org/10.18637/jss.v055.i08.

LeSage, James P. 2008. “An Introduction to Spatial Econometrics.” Revue d’économie Industrielle 123 (123): 19–44. https://doi.org/10.4000/rei.3887.

LeSage, James P., and R. Kelley Pace. 2014. “The Biggest Myth in Spatial Econometrics.” Econometrics 2 (4): 217–49. https://doi.org/10.2139/ssrn.1725503.

Lusher, Dean, Johan Koskinen, and Garry Robins. 2013. Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications. Cambridge University Press.

Milo, R., S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon. 2002. “Network Motifs: Simple Building Blocks of Complex Networks.” Science 298 (5594): 824–27. https://doi.org/10.1126/science.298.5594.824.

R Core Team. 2023. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Robins, Garry, Philippa Pattison, and Peter Elliott. 2001. “Network Models for Social Influence Processes.” Psychometrika 66 (2): 161–89. https://doi.org/10.1007/BF02294834.

Robins, Garry, Pip Pattison, Yuval Kalish, and Dean Lusher. 2007. “An introduction to exponential random graph (p*) models for social networks.” Social Networks 29 (2): 173–91. https://doi.org/10.1016/j.socnet.2006.08.002.

Roger Bivand. 2022. “R Packages for Analyzing Spatial Data: A Comparative Case Study with Areal Data.” Geographical Analysis 54 (3): 488–518. https://doi.org/10.1111/gean.12319.

Slaughter, Andrew J., and Laura M. Koehly. 2016. “Multilevel Models for Social Networks: Hierarchical Bayesian Approaches to Exponential Random Graph Modeling.” Social Networks 44: 334–45. https://doi.org/10.1016/j.socnet.2015.11.002.

Snijders, Tom A B, Philippa E Pattison, Garry L Robins, and Mark S Handcock. 2006. “New specifications for exponential random graph models.” Sociological Methodology 36 (1): 99–153. https://doi.org/10.1111/j.1467-9531.2006.00176.x.

Snijders, Tom A. B., and Stephen P. Borgatti. 1999. “Non-Parametric Standard Errors and Tests for Network Statistics.” Connections 22 (2): 1–10.

Stadtfeld, Christoph, Tom A. B. Snijders, Christian Steglich, and Marijtje van Duijn. 2020. “Statistical Power in Longitudinal Network Studies.” Sociological Methods & Research 49 (4): 1103–32. https://doi.org/10.1177/0049124118769113.

Stivala, Alex D., H. Colin Gallagher, David A. Rolls, Peng Wang, and Garry L. Robins. 2020. “Using Sampled Network Data With The Autologistic Actor Attribute Model.” arXiv. https://doi.org/10.48550/arXiv.2002.00849.

Stivala, Alex, Garry Robins, and Alessandro Lomi. 2020. “Exponential Random Graph Model Parameter Estimation for Very Large Directed Networks.” PLoS ONE 15 (1): 1–23. https://doi.org/10.1371/journal.pone.0227804.

Tanaka, Kyosuke, and George G. Vega Yon. 2024. “Imaginary Network Motifs: Structural Patterns of False Positives and Negatives in Social Networks.” Social Networks 78 (July): 65–80. https://doi.org/10.1016/j.socnet.2023.11.005.

Valente, Thomas W., and George G. Vega Yon. 2020. “Diffusion/Contagion Processes on Social Networks.” Health Education & Behavior 47 (2): 235–48. https://doi.org/10.1177/1090198120901497.

Valente, Thomas W., Heather Wipfli, and George G. Vega Yon. 2019. “Network Influences on Policy Implementation: Evidence from a Global Health Treaty.” Social Science and Medicine. https://doi.org/10.1016/j.socscimed.2019.01.008.

Vega Yon, George. 2020. ergmito: Exponential Random Graph Models for Small Networks. CRAN. https://cran.r-project.org/package=ergmito.

Vega Yon, George G. 2023. “Power and Multicollinearity in Small Networks: A Discussion of ‘Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks’ by Krivitsky, Coletti & Hens.” Journal Of The American Statistical Association.

Vega Yon, George G., Andrew Slaughter, and Kayla de la Haye. 2021. “Exponential Random Graph Models for Little Networks.” Social Networks 64 (August 2020): 225–38. https://doi.org/10.1016/j.socnet.2020.07.005.

Wang, Zeyi, Ian E. Fellows, and Mark S. Handcock. 2023. “Understanding Networks with Exponential-Family Random Network Models.” Social Networks, August, S0378873323000497. https://doi.org/10.1016/j.socnet.2023.07.003.

Wasserman, Stanley, and Philippa Pattison. 1996. “Logit models and logistic regressions for social networks: I. An introduction to Markov graphs and p*.” Psychometrika 61 (3): 401–25. https://doi.org/10.1007/BF02294547.

2 Disclaimer

This is an ongoing project. The course is being developed and will be updated as we go. If you have any comments or suggestions, please let me know. The generation of the course materials was assisted by AI tools, namely, GitHub copilot.

3 Code of Conduct

Please note that the networks-udd2024 project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.