Day 3 (AM session) – Foundations of Data Science II (Wednesday 7th December)
09.30:Dr Riccardo Rastelli, University College Dublin. Statistical network analysis I
Brief synopsis: Nowadays, large amounts of stored data describe how entities interact with each other. For example, these data may represent friendship relations between scholars, coauthorship relations between researchers, mutual claims between financial institutions, or functional connectivity between different areas of the brain. Random graphs are the mathematical tools that are used to represent these interaction datasets.Researchers and practitioners are often interested in modelling the random graphs, and in understanding their structures and capturing some of their features of interest. For these purposes, a number of statistical models have been introduced in recent times. One family of these statistical models relies on a “latent variable” structure, whereby one assumes that the nodes are characterised by some latent information that determines their connectivity behaviour. In this session, I will give an overview on some common latent variable models and methods that are used in the analysis of random graphs. In particular, the lecture will cover the stochastic block model, which is a widely used clustering model for networks. I will introduce the theory behind these models, and give an overview of their mathematical properties. In addition, I will give a demonstration on how these models can be fitted on network data, and how we can interpret the modelling results.I will provide some code for the lab demonstration . If the students would like to run this code, they will require a laptop with R installed https://www.r-project.org/ with additional R packages: igraph, statnet, latentnet, blockmodels, dynsbm
Speaker biography: Dr. Riccardo Rastelli is Lecturer at the School of Mathematics and Statistics, University College Dublin. His research focuses on statistical methodologies for the analysis of complex networks.
11.30: Dr Isabella Gollini, University College Dublin. Statistical network analysis II
Brief synopsis: This hands-on session will provide participants with an overall understanding of two families of statistical models for the analysis of relational data: exponential random graph models and latent space models. Data analysis will be carried out by analysing real-world data.Must have R installed and the R package “statnet” https://cloud.r-project.org/
Speaker biography: Dr Isabella Gollini is an Assistant Professor in Statistics at the UCD School of Mathematics and Statistics, and Associate Editor for the Journal of Statistical Software and The R Journal. Isabella works closely with scientists with different expertise in order to yield high-impact practical results as well as developments of statistical and computational methods and software packages. Isabella is active in organising national and international events to promote diversity in the Statistics, Data Science, Network Science, and R communities. She is mentor at R-Ladies and was teaching leader at Forwards the R Foundation taskforce on women and other under-represented groups in 2017-2020.
Please note 0.5 of a training day will be deducted from the annual training allowance for each Enterprise Alliance attendee
The SFI Centre for Research Training in Foundations of Data Science will train a cohort of PhD students with world-class foundational understanding in the horizontal themes of Applied Mathematics, Statistics, and Machine Learning.
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