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 Prof. Gesine Reinert, Turing Institute Talk Prof. Gesine Reinert, Master Class "Inference of Networks Prof. Gesine Reinert, "Estimating the number of communities in a network Dr. Veronica Vinciotti, "Sparse Gaussian graphical models for dynamic gene regulatory networks Prof. Ernst Wit, "Network inference in genomics Prof. Neil Friel, "Properties of Latent Variable Network Models Dr. Alberto Caimo, "Bayesian ERGMs -- computational and modelling challenges Dr. Pariya Behrouzi, "Detecting Epistatic Selection in the Genome of RILs via a latent Gaussian Copula Graphical Model Prof. Alberto Roverato, "The Networked Partial Correlation and its Application to the Analysis of Genetic Interactions Dr. Reza Mohammadi, "Bayesian modelling of Dupuytren disease using Gaussian copula graphical models Dr. Silvia Fierascu, "Applying network science to political problems. A conceptual and analytical framework for understanding and predicting corruption risks in business-political networks Dr. Ben Parker, "Optimal Design of Experiments on Connected Units with Application to Social Networks Prof. Tom Snijders, "Continuous-time statistical models for network panel data Prof. Eric Kolazyk, "Dynamic causal networks with multi-scale temporal structure Prof. Tom Britton, "A network epidemic model with preventive rewiring: comparative analysis of the initial phase Prof. Stephane Robin, "Detecting change-points in the structure of a network: Exact Bayesian inference Dr. Catherine Matias, "Statistical clustering of temporal networks through a dynamic stochastic block model Prof. Niel Lawrence, "Deep Probabilistic Modelling with Gaussian Processes



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