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Below are a selection of papers describing some of the work we have done here in Melbourne on network models using snowball sampled data, including several empirical applications. We have an ongoing project for a more systematic study of inferential validity for Autologistic Actor Attribute Models using snowball samples, but more to come on that in the future.





Professor Garry Robins, FASSA,

Melbourne School of Psychological Sciences

University of Melbourne

Victoria 3010


Adjunct Professor, Swinburne University, Melbourne, Australia.

Personal website:

Melnet website:


Check out my book on Social Network Research. Doing Social Network Research: Network-based research design for social scientists.Sage books



Bryant, R. A., Gallagher, H. C., Gibbs, L., Pattison, P., MacDougall, C., Harms, L., ... & Richardson, J. (2017). Mental health and social networks after disaster. American Journal of Psychiatry, 174(3), 277-285.

Daraganova, G. & Pattison, P (2013). Autologistic actor attribute model analysis of unemployment: dual importance of who you know and where you live. In Lusher, D, Koskinen, J., & Robins, G. (Eds.) Exponential random graph models for social networks (pp. 237-247). New York, NY: Cambridge.

Kashima, Y., Wilson, S., Lusher, D., Pearson, L. J., & Pearson, C. (2013). The acquisition of perceived descriptive norms as social category learning in social networks. Social Networks, 35(4), 711-719.

Pattison, P. E., Robins, G. L., Snijders, T. A., & Wang, P. (2013). Conditional estimation of exponential random graph models from snowball sampling designs. Journal of Mathematical Psychology, 57(6), 284-296.

Rolls, D., Wang, P., Jenkinson, R., Pattison, P., Robins, G., Sacks-Davis, R., Daraganova, G., Hellard, M., & McBryde, E. (2013). Modelling a disease-relevant contact network of people who inject drugs. Social Networks, 35, 699-710.

Rolls, D. A., & Robins, G. (2017). Minimum distance estimators of population size from snowball samples using conditional estimation and scaling of exponential random graph models. Computational Statistics & Data Analysis116, 32-48.

Stivala, A. D., Koskinen, J. H., Rolls, D. A., Wang, P., & Robins, G. L. (2016). Snowball sampling for estimating exponential random graph models for large networks. Social Networks, 47, 167-188.




---------- Forwarded message ---------
From: Greg Doyle <[log in to unmask]>
Date: Wed, 1 May 2019 at 17:37
Subject: [SOCNET] Empirical research using snowball sampling to examine power and influence
To: <[log in to unmask]>

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I am looking for sociology-related papers that describe empirical projects examining influence or power in real-world human social networks constructed through snowball sampling from multiple random seed nodes.

I am particularly interested in papers that discuss the implications of missing data, the validity of centrality measures, and boundary specification for extrapolating beyond the sample to the actual social context it represents.

I'm trying to understand the problems that arise when making the case that a snowball-sampled network is a reasonable proxy for a real social world - especially around the issue of how large a sample needs to be relative the estimate size of the total social world in question and how many seed nodes are required. For example, if one wanted to find the most central doctor out of all the doctors in a town is it possible to sample less than 100 % of all the doctors and if so how to estimate the minimum % required and the minumum number of start points.

Thanks very much

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