Find books, journal articles and other MelNet publications

ERGM Book

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.

Multilevel networks

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.

See our representative list of publications by the MelNet team below

• Block, P., Hollway, J., Stadtfeld, C. J., Koskinen, Snijders, T.A.B. (2022). Circular specifications and “predicting” with information from the future: Errors in the empirical SAOM–TERGM comparison of Leifeld & Cranmer (2019a), Network Science (to appear). doi:10.1017/nws.2022.6

• Koskinen, J.H., & Snijders, T.A.B. (2022). Multilevel Longitudinal Analysis of Social Network. arXiv:2201.12713

• Stys, P., Muhindo, S., Balume, B., N’simire, S., and Koskinen, J. (2022). Trust, quality, and the network collection experience: A tale of two studies on the Democratic Republic of the Congo, Social Networks, 68: 237-255. doi.org/10.1016/j.socnet.2021.08.002

• Brocattelli, Chiara, Peng Wang, Lisa McDaid, Mark McCann, Sharon Anne Simpson, Lawrie Elliott, Laurence Moore & Kirstin Mitchell (2021). Social Network Research contribution to evaluating process in a feasibility study of a peer-led and school-based sexual health intervention. Scientific Reports volume 11, Article number: 12244.

• Sadewo, Giovanni Radhitio, Kashima, Emiko, Gallagher, Colin, Kashima, Yoshihisa, Koskinen, Johan (2020). International Students’ Cross-Cultural Adjustment: Social Selection or Social Influence? Journal of Cross-Cultural Psychology (accepted for publication 2/5/2020). https://doi.org/10.1177/0022022120930092

• Coutinho, J., Diviák, T., Stivala, A. (2020). A Man’s World? Comparing the Structural Positions of Men and Women in an Organized Criminal Network. Crime, Law and Social Change. (Accepted for publication July 2020; publisher webpage)

• Jones, P., Quinn, E., Koskinen, J. (2020). Measuring centrality in film narratives using dynamic character interaction networks, Social Networks, 63: 21-37. https://doi.org/10.1016/j.socnet.2020.03.003.

• Koskinen, J.H., & Daraganova, G. (2020). Bayesian Analysis of Social Influence. arXiv:2006.16464. JRSS Series A (to appear). DOI: 10.1111/rssa.12844

• Coutinho, J., Diviák, T., Bright, D., Koskinen, j. (2020). Multilevel Determinants of Collaboration Between Organised Criminal Groups. Social Networks, 63: 56-69. https://doi.org/10.1016/j.socnet.2020.04.002

• Sohn, C., Christopoulos, D., and Koskinen, J. (2019). Borders moderating distance: A Social Network Analysis of Spatial Effects on Policy Interactions, Geographical Analysis. DOI: 10.1111/gean.12218

• Stys, P., Verweijen, J., Muzuri, P., Muhindo, S., Vogel,C., and Koskinen, J. (2020). Brokering Between (not so) Overt and (not so) Covert Networks in Conflict Zones, Global Crime, 21(1): 74-110. DOI: 10.1080/17440572.2019.1596806

• Gallagher, H. C., Block, K., Gibbs, L., Forbes, D., Lusher, D., Molyneaux, R., Richardson, J., Pattison, P., MacDougall, C., & Bryant, R. (2019). The effect of group involvement on post-disaster mental health: A longitudinal multilevel analysis. Social Science and Medicine, 220, 167-175. https://doi.org/10.1016/j.socscimed.2018.11.006

• Gallagher, H. C. (2019). Social networks and the willingness to communicate: Reciprocity and brokerage. Journal of Language and Social Psychology, 38, 194–214. https://doi.org/10.1177%2F0261927X18809146

• Kashima, Y., Bratanova, B., & Peters, K. (2018). Social transmission and shared reality in cultural dynamics. Current opinion in psychology, 23, 15-19.

• 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. (pre-peer reviewed)

• 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.

• Gallagher, H. C., Lusher, D., Gibbs, L., Pattison, P., Forbes, D., Block, K., Harms, L., MacDougall, C., Kellett, C., Ireton, G., & Bryant, R. A. (2017). Dyadic effects of attachment on mental health: Couples in a postdisaster context. Journal of Family Psychology, 31, 192-202. http://dx.doi.org/10.1037/fam0000256

• Bryant, R.A., Gallagher, H. C., Gibbs, L., Pattison, P. MacDougall, Harms, L., Block, K., Baker, E., Sinnott, V., Ireton, G., Richardson, J., Forbes, D., Lusher, D. (2017). Mental health and social networks following disaster. American Journal of Psychiatry. doi.org/10.1176/appi.ajp.2016.15111403

• 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.

• Broccatelli, C., Koskinen, J., & Everett, M. (2016). Temporal Dynamics in Covert Networks. Methodological Innovations, 9:1-14.

• 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.

• Crossley N., Bellotti, E., Edwards, G., Everett, M., Koskinen, J., & Tranmer, M. (2015). Social Network Analysis for Ego-Nets. SAGE, London.

• 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

• Koskinen, J. H. (2008). The Linked Importance Sampler Auxiliary Variable Metropolis Hastings Algorithm for Distributions with Intractable Normalising Constants. MelNet Social Networks Laboratory Technical Report 08-01, Department of Psychology, School of Behavioural Science, University of Melbourne, Australia.

• Robins, G. L., Pattison, P. E. & Koskinen, J. H. (2008). Network Degree Distributions, MelNet Social Networks Laboratory Technical Report 08-02, Department of Psychology, School of Behavioural Science, University of Melbourne, Australia

• 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., Robins, G. L., & Pattison, P. E. (2007). Missing data in social network analysis: Inferences from incomplete missing data. Interim Report for DSTO (73 pages).

• 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.

• Robins, G., Elliott, P., & Pattison, P. (2001). Network models for social selection processes. Social Networks, 23, 1–30.

• Pattison, & Wasserman, S. (1999). Logit models and logistic regressions for social networks: II. Multivariate relations. British Journal of Mathematical & Statistical Psychology, 52, 169-193.

• Robins, G., Pattison, P., & Wasserman, S. (1999). Logit models and logistic regressions for social networks: III. Valued relations. Psychometrika, 64, 371–394.

• Wasserman, S., & Pattison, P. (1996). Logit models and logistic regression for social networks: I. An introduction to Markov graphs and p?. Psychometrika, 61, 401–425.