The ANSNA Pip Pattison Research Award
The ANSNA Pip Pattison Award recognises significant achievement in Australian social network research. It is awarded each year to a senior or middle career researcher from an Australian research institution to acknowledge significant contributions to (1) the scientific study of social networks and (2) raising the profile of social network research in Australia.
Pip Pattison’s contribution to Australian social network research has been profound. Her methodological and theoretical work has given her a leading international reputation, with an exceptional list of influential publications, research grants and major collaborations. Especially through her establishment of the Social Networks research laboratory at the University of Melbourne, she has supervised and mentored many Australian network researchers and has been an always available source of advice and wisdom.
2023 Winner
Dr Johan Koskinen
Dr Johan Koskinen is Lecturer in Statistics at Stockholm University, having previously held positions at the universities of Melbourne, Oxford and Linköping. He develops statistical models and inference for social networks and often works in close collaboration with subject area experts to infer underlying network processes for empirical data, preferably within a Bayesian framework. Together with colleagues in Melbourne he edited the 2013 book on Exponential random graph models (Cambridge University Press, 2012), which was awarded the Harrison White Book Award. He has contributed to the publicly available programs MPNet and RSiena, and has been active in delivering training in network analysis across the world. Of particular interest to him is imperfectly observed network data and computational methods for networks on different types of ties and nodes, in space and across time.
2022 Winner
The 2022 Pip Pattison Award was not awarded, as the International Network of Social Network Analysis Sunbelt Conference took place in Cairns, Australia.
2021 Winner
Margaret Hellard AM
Professor Margaret Hellard AM is a Deputy Director at the Burnet Institute, Head of Hepatitis Services in Department of Infectious Diseases at The Alfred Hospital and an Adjunct Professor at Monash University and University of Melbourne, Melbourne, Australia.
Margaret’s principal research interests are in preventing the transmission and improving the management of blood borne viruses; the ultimate aim being to eliminate viral hepatitis as a public health threat and end the AIDS epidemic. More recently she is also undertaking work to reduce the impact of the COVID-19 epidemic on the community. This has included exploring the role of social networks in disease transmission and prevention.
Margaret is a member of numerous advisory committees and working groups on viral hepatitis and HIV within Australia and globally, including Co-Chairing the WHO Strategic and Technical Advisory Group on HIV and Viral Hepatitis.
2020 Inaugural Winner
Lucia Falzon
Lucia is a Senior Fellow of the Defence Science and Technology Group (DSTG) in the Australian Department of Defence. In this role she provides guidance and mentoring to DSTG researchers and contributes directly to collaborative initiatives with other research institutions. Since joining Defence in 1990 and up to her retirement in 2019, Lucia was involved in various research initiatives aimed at developing modelling and analysis tools to support Defence and National Security, with a particular focus on social network analysis. Her current research is on methods for temporal analysis of interaction networks and the formal characterisation of social media as a system for spreading information and social attitudes. She is an Honorary Fellow at the School of Psychological Sciences, University of Melbourne, and a Visiting Fellow at The Centre for Defence Communications and Information Networking at the University of Adelaide.
The ANSNA Garry Robins Early Career Research Award
The ANSNA Garry Robins Early Career Research Award, launched in 2023, recognises significant achievement of an early or mid career researcher in Australian social network research. It will be awarded annually to an early/mid career scholar from an Australian research institution to acknowledge contributions to (1) the scientific study of social networks and (2) to the Australian social network research community.
The award will be given to a scholar who is in the earlier stages of their career (i.e. someone who has received their PhD within the past 15 years, accounting for any career interruptions, e.g. family, health, etc.).
Garry Robins’ contributions to social network analysis in Australia have been profound, not least through the supervision and mentoring of a generation of scholars in the field. His theoretical and methodological innovations are recognised for their significance internationally, and in 2016 he was awarded the Simmel Award.
The first ANSNA Early Career Research Award winner, Assoc. Prof Peng Wang, was announced at ANSNA’s Australian Social Network Analysis Conference (ASNAC) in Sydney in 2023. The award recipient will give an opening lecture at the following ASNAC in 2024; this lecture will review the work for which the award was given. Expenses for conference registration in 2024 will be part of the award.
2023 Inaugural Winner
Peng Wang
Peng is Associate Professor of Innovation Studies at Swinburne University of Technology. His research focuses on the methodology development for statistical models for social networks, including Exponential Random Graph Models (ERGM) for social selection processes, and Auto-logistic Actor Attribute Models (ALAAM) for social influence processes. Peng is the designer and programmer for the PNet suite of software package for the statistical modelling of one-mode, two-mode, multiplex, multilevel networks, as well as the co-evolution of network structure and individual outcomes. Peng has applied these models in the research fields of public health, education, management, social-ecological systems, interlocking directorates, public policy, and social network intervention evaluations.