**Bio:**

Tom A.B. Snijders (http://www.stats.ox.ac.uk/~snijders/) is professor of Statistics and Methodology in the Social Sciences at the University of Groningen and emeritus fellow of Nuffield College, University of Oxford.

He studied mathematics and obtained a PhD in 1979 from the University of Groningen with a dissertation in mathematical statistics. His research concentrates on social network analysis and multilevel analysis. His work on developing statistical methodology for network dynamics is implemented in the software package RSiena (*Simulation Inference for Empirical Network Analysis*) in the statistical system R. With Roel J. Bosker he wrote *Multilevel Analysis; An Introduction to Basic and Advanced Multilevel Modeling *(Sage, 2^{nd} ed., 2012). Combining these two research strands, together with Emmanuel Lazega he edited *Multilevel Network Analysis for the Social Sciences; Theory, Methods and Applications *(Springer, 2016). Together with Patrick Doreian he was co-editor of *Social Networks* from 2006 to 2011. He supervised and co-supervised more than 60 PhD dissertations. From 2002 to 2006 he was scientific director of the graduate school ICS (*Inter-university Center for Social Science Theory and Methodology*). He received two awards from INSNA (*International Network for Social Network Analysis*): the Georg Simmel Award in 2010 and the Bill Richards software award in 2017; and honorary doctorates from the University of Stockholm (2005) and the Université Paris-Dauphine (2005).

**Topic:**

Empirical Methods and Networks

For matchmaking, it is important that the partners in the match understand each others’ background; therefore my contribution will include a historical element.

“Social networks” has been in the social sciences
a niche subject, but an important and extended niche, that took off in the
1970s. The journal *Social Networks* dates
from 1978. Its founder and long-time editor, Linton Freeman, defined the field
as being motivated by a structural intuition based on ties linking social
actors, grounded in systematic empirical data, drawing on graphic imagery, and
relying on mathematical and computational models. Empirical methods therefore
have been essential to the field right from the start.

As to mathematical modelling, statistical methods for a long time contributed quite a small portion, much smaller than discrete mathematics and graph theory; but statistical methods started becoming important for empirical social network research (with the stress on “social” as in social sciences) in the 1990 and 2000s. This was possible in the first place by the development of the Exponential Random Graph Model (‘ERGM’), which has become an important workhorse for social network analysis. I shall try to explain the main features of this model, combining the points of view of a statistician and of a sociologist.

The ERGM was originally meant for representing data of one network. Networks are inherently dynamic, however, and longitudinal network data have been collected more and more. Also, the importance of social networks in the social sciences is based on the fact that they influence much human activity, but also are influenced by human activity. This leads to scientific interest in the dynamics of networks, and in the co-evolution of networks and individual-level variables. This can be modelled by the Stochastic Actor-Oriented Model (‘SAOM’). I shall also try to present the main features of this model, and of other temporal approaches to network modelling.