A major challenge in many modern economic, epidemiological, ecological and biological questions is to understand the randomness in the network structure of the entities they study. Although analysis of data on networks goes back to at least the 1930s, the importance of statistical network modelling for many areas of substantial science has become more pronounced since the turn of the century.  This Committee on Statistical Network Science (CSNS) focuses on promoting and fostering research in statistical and probabilistic network analysis, in the wider sense. This remit includes graphical models, random graph models as well complex functional network models.

Committee Members:

• (Chair) Veronica Vinciotti (University of Trento, Italy)

• Vladimir Batagelj (University of Ljubljana, Slovenia) 

• Carter Butts (UC Irvine, USA)

• Clelia Di Serio (San Raffaele Vita-Salute University, Italy)

• Alexander Fuchs-Kreiß (Hildesheim University, Germany)

• Sarika Jalan (Indian Institute of Technology, India)

• Goeran Kauermann (Ludwig-Maximilians-Universität München, Germany)

• Catherine Matias(Sorbonne Université, France) 

• Nynke Niezink (Carnegie Mellon, Pittsburgh, USA)

• Riccardo Rastelli (University College Dublin, Ireland)

• (former Chair) Gesine Reinert (University of Oxford, UK)

• Ernst C. Wit (Università della Svizzera italiana, Switzerland)

Activities of the committee

Please follow the activities of the committee at https://github.com/BS-SNS/Public

Some video links

Can we use network models to shed new light on global arms trading?
Using statistical network models to understand the driving forces in arms trading in the last decades and today.
https://www.youtube.com/watch?v=kxzJ9RFSCJ0

Can we improve social support for the elderly during COVID-19?
This research highlights which groups of elderly people have either non-existent or insufficient social support during the COVID-19 pandemic and are therefore more vulnerable. For these elderly people, sustainable care policy planning and crisis intervention planning should be organised especially for future waves of the coronavirus and other pandemics.
https://www.youtube.com/watch?v=5tIWuBvA6NI

Normal or not? How to detect anomalies in networks
Complex interactions such as financial transactions or links between computers can often be visualised as networks. Anomalies in such networks may indicate deviant behaviour. How can we detect such anomalies?
In this video we shall encounter a statistical test called Monte Carlo test to address this issue. The Monte Carlo test can also be applied in many other scenarios.
https://sciani.com/portfolio/normal-or-not-how-to-detect-anomalies-in-networks/

Sometimes, correlation does imply causation
You often hear “correlation does not imply causation” in order to warn the listener about spurious relationships that may be observed in everyday life. The number of homicides seems to correlate with ice cream sales, but clearly this is not a causal relationship. Given that all we can see in real life are mere correlations, can we ever be sure of causality? This video will describe a network inspired definition of causality in order to derive a method, known as the PC algorithm, to detect causal interactions.
https://www.youtube.com/watch?v=XoLxQfi194c

Some online seminars

https://www.youtube.com/watch?v=l1iSbsQjWxo Prof. Gesine Reinert, Turing Institute Talk

https://www.youtube.com/watch?v=Ha32LmYLmrU Prof. Gesine Reinert, Master Class "Inference of Networks"

https://www.youtube.com/watch?v=_bTieZBfdf8 Prof. Gesine Reinert, "Estimating the number of communities in a network"

https://www.youtube.com/watch?v=G-ajej8mI34 Dr. Veronica Vinciotti, "Sparse Gaussian graphical models for dynamic gene regulatory networks"

https://www.youtube.com/watch?v=qdxsEt_bHNI Prof. Matthias Drton, “Parameter identification in linear non-Gaussian causal models under general confounding”

https://www.youtube.com/watch?v=e4C_4fkmb3Y Dr. Veronica Vinciotti, “Causal invariance through Pearson risk invariance”

https://www.youtube.com/watch?v=i7ynUZ10v-k Prof. Ernst Wit, "Causal invariance and beyond"

https://www.youtube.com/watch?v=cFj-_b-u0Ak Dr. Alberto Caimo, "Bayesian ERGMs -- computational and modelling challenges"

https://www.youtube.com/watch?v=CwfWr62oMno Prof. Alberto Roverato, "The Networked Partial Correlation and its Application to the Analysis of Genetic Interactions"

https://www.youtube.com/watch?v=qY5urxF6EKo Dr. Silvia Fierascu, "Applying network science to political problems. A conceptual and analytical framework for understanding and predicting corruption risks in business-political networks"

https://www.youtube.com/watch?v=lEyjl6VWNaI Dr. Ben Parker, "Design of experiments for social networks”

https://www.youtube.com/watch?v=3h9ODIBjCAI Prof. Tom Snijders, "Continuous-time statistical models for network panel data"

https://www.youtube.com/watch?v=Mrfgz9CPOco Prof. Eric Kolazyk, "Dynamic causal networks with multi-scale temporal structure"

https://www.youtube.com/watch?v=60WMopCXRd8 Prof. Tom Britton, "A network epidemic model with preventive rewiring: comparative analysis of the initial phase"

https://www.youtube.com/watch?v=sTRvcU6sZNk Prof. Stephane Robin, "Detecting change-points in the structure of a network: Exact Bayesian inference"

https://www.youtube.com/watch?v=YvEgTaCcC8A Dr. Catherine Matias, "Statistical clustering of temporal networks through a dynamic stochastic block model"

https://www.youtube.com/watch?v=NHTGY8VCinY Prof. Niel Lawrence, "Deep Probabilistic Modelling with Gaussian Processes ostnet.webhosting.rug.nl/dokuwiki/doku.php?id=pub:online

• 2023: QIYANG HAN (Rutgers, The State University of New Jersey), who delivered the Kendall Lecture at the RSS Annual International Conference (Brighton, 2-5 September 2024).
• 2021: Ewain Gwynne, University of Chicago
  who delivered the Kendall Lecture during the RSS International Conference 2022, September 12-15, 2022 in Aberdeen.