Statistical Network Science Committee
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) will focus 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.
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.
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.
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.
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.
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.newton.ac.uk/seminar/20160727093010001 Prof. Gesine Reinert, "Estimating the number of communities in a network
https://www.newton.ac.uk/seminar/20161214111512001 Dr. Veronica Vinciotti, "Sparse Gaussian graphical models for dynamic gene regulatory networks
https://www.newton.ac.uk/seminar/20160824113012301 Prof. Ernst Wit, "Network inference in genomics
https://www.newton.ac.uk/seminar/20160715113012001 Prof. Neil Friel, "Properties of Latent Variable Network Models
https://www.newton.ac.uk/seminar/20160728091510152 Dr. Alberto Caimo, "Bayesian ERGMs -- computational and modelling challenges
https://www.newton.ac.uk/seminar/20160825104011001 Dr. Pariya Behrouzi, "Detecting Epistatic Selection in the Genome of RILs via a latent Gaussian Copula Graphical Model
https://www.newton.ac.uk/seminar/20160826090009401 Prof. Alberto Roverato, "The Networked Partial Correlation and its Application to the Analysis of Genetic Interactions
https://www.newton.ac.uk/seminar/20160826094010201 Dr. Reza Mohammadi, "Bayesian modelling of Dupuytren disease using Gaussian copula graphical models
https://www.newton.ac.uk/seminar/20160826102010401 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.newton.ac.uk/seminar/20160826133014101 Dr. Ben Parker, "Optimal Design of Experiments on Connected Units with Application to Social Networks
https://www.newton.ac.uk/seminar/20161212093010301 Prof. Tom Snijders, "Continuous-time statistical models for network panel data
https://www.newton.ac.uk/seminar/20161212111512001 Prof. Eric Kolazyk, "Dynamic causal networks with multi-scale temporal structure
https://www.newton.ac.uk/seminar/20161213093010301 Prof. Tom Britton, "A network epidemic model with preventive rewiring: comparative analysis of the initial phase
https://www.newton.ac.uk/seminar/20161213160016451 Prof. Stephane Robin, "Detecting change-points in the structure of a network: Exact Bayesian inference
https://www.newton.ac.uk/seminar/20161215160016451 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
Last Updated: Monday, 14 February 2022 22:12