Find books, journal articles and other MelNet publications
Exponential random graph models (ERGMs) are increasingly applied to observed network data and are central to understanding social structure and network processes. This edited volume provides a self-contained, exhaustive account of the theoretical and methodological underpinnings of ERGMs.
Doing Social Network Research
With straight-forward guidance on research design and data collection, as well as social network analysis, this book takes you start to finish through the whole process of doing network research.
Networks provide a more complex representation of interdependence. In our research we extend exponential random graph models (ERGMs) to multilevel networks. We present a general formulation of a multilevel network structure.
Social Network Analysis for Ego-Nets
Ego networks are a form of network data where relational ties are only collected for a set of independent, distinct, focal actors. Ties among their nominated contacts occasionally also are elicited. This book is a comprehensive treatment of ego networks, from introductory definitions and theory to advanced, statistical longitudinal analysis.
See our representative list of publications by the MelNet team below
Koskinen, J., Wang, P., Robins, G., & Pattison, P. (2018). Outliers and Influential Observations in Exponential Random Graph Models. Psychometrika, 83(4): 809-830.
Everett, M.G., Borgatti, S.P., Brocattelli, C., & Koskinen, J.H. (2018). Measuring Knowledge and Experience in Two Mode Temporal Networks. Social Networks, 55: 63-73.
Koskinen, J.H. (2018), Discussion of "Optimal treatment allocations in space and time for on-line control of an emerging infectious disease" by Laber, N. J. Meyer, B. J. Reich, K. Pacifici, J. A. Collazo and J. Drake, J.R.Statist.Soc. C, 67: 779.
Müller, T., Grund, T., & Koskinen, J. (2018). Residential segregation and ‘ethnic flight’ vs. ‘ethnic avoidance’ in Sweden, European Sociological Review, 34, 268-285.
Bright, D , Koskinen, J., Malm, A. (2018). Illicit network dynamics: The formation and evolution of a drug trafficking network, Journal of Quantitative Criminology.
Block, P., Koskinen, Stadtfeld, C. J., Hollway, J., Steglich, C. (2018). Change we can believe in: Comparing Longitudinal Network Models on Consistency, Interpretability and Predictive Power, Social Networks, 52: 180-191.
Bryant, R.A., Gallagher, H. C., Gibbs, L., Pattison, P. … Lusher, D. (2017). Mental health and social networks following disaster. American Journal of Psychiatry. Advance online publication.
Koskinen, J., Müller, T., & Grund, T. (2017). A dynamic discrete-choice model for movement flows. Pp.: 107-117 in Perna, C., Pratesi, M. & Ruiz-Gazen, A. (eds.), Studies in Theoretical and Applied Statistics. Springer
Stivala, A., Koskinen, J., Rolls, D., Wang, P., & Robins, R. (2016). Snowball sampling for estimating exponential random graph models for large networks. Social Networks 47: 167-188.
Brennecke, J., & Rank, O. N. (2016): The interplay between formal project memberships and informal advice seeking in knowledge-intensive firms: A multilevel network approach. Social Networks.
Agneessens. F., Koskinen J. (2016). Modelling individual outcomes using a multilevel social influence (MSI) model, p 81-105 in Emmanuel Lazega and Tom Snijders (Eds.) Multilevel Network Analysis for the Social Sciences. Springer: London.
Hollway, J., & Koskinen J. (2016). Multilevel Embeddedness: The Case of the Global Fisheries Governance Complex. Social Networks, 44: 281-294.
Robins, G. (2015). Doing Social Networks Research: Network Research Design for Social Scientists. Los Angeles: Sage.
Gallagher, H. C., & Robins, G. (2015). Network statistical models for language learning contexts: Exponential random graph models and willingness to communicate. Language Learning, 65 (4): 929-962.
Koskinen J., Caimo, A., & Lomi, A. (2015). Simultaneous modeling of initial conditions and time heterogeneity in dynamic networks: An application to Foreign Direct Investments. Network Science, 3(1): 58-77.
Brailly J., Favre G., Chatellet J., Lazega E. (2015). Embeddedness as a Multilevel Problem. A Case Study in Economic Sociology. Social Networks
Lomi, A., Lusher, D., Pattison, P., & Robins, G. L. (2014). The focused organization of advice relations: A case study of boundary-crossing ties in a multi-unit business group. Organization Science, 25(2).
Koskinen, J. H., Robins, G. L., Wang, P., Pattison, P. E. (2013). Bayesian analysis for partially observed network data, missing ties, attributes and actors. Social Networks, vol. 35(4), 514-527.
Lusher, D., Koskinen, J., & Robins, G. (Eds.). (2013). Exponential Random Graph Models for Social Networks: Theory, Methods and Applications. New York: Cambridge University Press.
Wang, P., Robins, G., Pattison, P., & Lazega, E. (2013). Exponential random graph models for multilevel networks. Social Networks, 35(1), 96-115.
Koskinen, J., & Lomi, A. (2013). The Local Structure of Globalization: The Network Dynamics of Foreign Direct Investments in the International Electricity Industry. Journal of Statistical Physics. 151(3): 523-548.
Lusher, D., Robins, G, Pattison, P., & Lomi, A. (2012). "Trust Me": Social Mechanisms for Expressed and Perceived Trust in an Organization. Social Networks, 34, 410-424.
Daraganova, G., Pattison, P., Koskinen, J., Mitchell, B., Bill, A., Watts, M., & Baum, S. (2012). Networks and geography: modelling community network structures as the outcome of both spatial and network processes. Social Networks. Vol. 34 (1), 6-17.
Koskinen, J. & Edling, C. (2012). Modelling the evolution of a bipartite network—Peer referral in interlocking directorates. Social Networks, Vol. 34 (3), 309–322.
Koskinen, J.H., and Stenberg, S-Å, (2012). Bayesian Analysis of Multilevel Probit Models for Data with Friendship Dependencies. Journal of Educational and Behavioural Statistics. 37(2): 203–230.
Snijders, T.A.B., Koskinen, J.H., & Schweinberger, M. (2010). Maximum likelihood estimation for social network dynamics. The Annals of Applied Statistics, Vol. 4(2): 567–588.
Koskinen, J. H., Robins, G. L., & Pattison, P. E. (2010). Analysing Exponential Random Graph (p-star) Models with Missing Data Using Bayesian Data Augmentation. Statistical Methodology, Vol. 7(3): 366-384.
Wang, P., Sharpe, K., Robins, G. L., & Pattison, P. E. (2009). Exponential random graph (p*) models for affiliation networks. Social Networks, 31(1), 12-25.
Robins, G., Pattison, P., & Wang, P. (2009). Closure, connectivity and degree distributions: Exponential random graph (p*) models for directed social networks. Social Networks, 31(2), 105-117. doi: 10.1016/j.socnet.2008.10.006
Robins, G., & Morris, M. (2007). Advances in exponential random graph (p*) models. Social Networks, 29(2), 169-172.
Robins, G., Pattison, P., Kalish, Y., & Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social Networks, 29(2), 173-191.
Robins, G., Snijders, T., Wang, P., Handcock, M., & Pattison, P. (2007). Recent developments in exponential random graph (p*) models for social networks. Social Networks, 29(2), 192-215. doi: 10.1016/j.socnet.2006.08.003
Koskinen, J.H. & Snijders, T.A.B. (2007). Bayesian Inference for Dynamic Social Network Data. Journal of Statistical Planning and Inference, 137(12): 3930-3938.
Snijders, T. A. B., Pattison, P. E., Robins, G. L., & Handcock, M. S. (2006). New specifications for exponential random graph models. In R. M. Stolzenberg (Ed.), Sociological Methodology 2006, Vol 36 (Vol. 36, pp. 99-153).
Pattison, & Robins, G. (2002). Neighbourhood-based models for social networks. Sociological Methodology, 32, 301-337.
Pattison, & Wasserman, S. (1999). Logit models and logistic regressions for social networks: II. Multivariate relations. British Journal of Mathematical & Statistical Psychology, 52, 169-193.