Complicated Networks

Valued, Multilayer, Multilevel, Multimode

Networks are more than simple graphs. We can ask if two people spoke to each other—but we can also ask how many times. We can ask who someone is friends with—but also who is their best friend, second best friend, and so on. We can ask about enemies as well. We can ask about different types of relationships: advice, support, influence. I seek to expand the scope of network and relation types that can be modelled statistically.

David Dekker, David Krackhardt, Patrick Doreian, and Pavel N. Krivitsky, Balance correlations, agentic zeros, and networks: The structure of 192 years of war and peace, PLOS ONE, vol. 19, no. 12, pp. 1–32, 2024. doi:10.1371/journal.pone.0315088

Pavel N. Krivitsky, David R. Hunter, Martina Morris, and Chad Klumb, ergm 4: New features for analyzing exponential-family random graph models, Journal of Statistical Software, vol. 105, no. 6, pp. 1–44, 2023. doi:10.18637/jss.v105.i06

Pavel N. Krivitsky and Carter T. Butts, Exponential-family random graph models for rank-order relational data, Sociological Methodology, vol. 47, no. 1, pp. 68–112, 2017. doi:10.1177/0081175017692623

Luke Mazur, Thomas Suesse, and Pavel N. Krivitsky, Investigating foreign portfolio investment holdings: Gravity model with social network analysis, International Journal of Finance & Economics, vol. 27, no. 1, pp. 554–570, 2020. doi:10.1002/ijfe.2168

Pavel N. Krivitsky, Exponential-family random graph models for valued networks, Electronic Journal of Statistics, vol. 6, pp. 1100–1128, 2012. doi:10.1214/12-EJS696

Pavel N. Krivitsky, Mark S. Handcock, Adrian E. Raftery, and Peter D. Hoff, Representing degree distributions, clustering, and homophily in social networks with latent cluster random effects models, Social Networks, vol. 31, no. 3, pp. 204–213, 2009. doi:10.1016/j.socnet.2009.04.001

Pavel N. Krivitsky and Mark S. Handcock, Fitting position latent cluster models for social networks with, Journal of Statistical Software, vol. 24, no. 5, pp. 1–23, 2008. doi:10.18637/jss.v024.i05

ergm: Fit, Simulate and Diagnose Exponential-Family Models for Networks

ergm.multi: Fit, Simulate and Diagnose Exponential-Family Models for Multiple or Multilayer Networks

ergm.count: Fit, Simulate and Diagnose Exponential-Family Models for Networks with Count Edges

ergm.rank: Fit, Simulate and Diagnose Exponential-Family Models for Rank-Order Relational Data

latentnet: Latent Position and Cluster Models for Statistical Networks