Associate Professor (Reader) in Machine Learning
School of Mathematics, The University of Edinburgh
The Maxwell Institute for Mathematical Sciences
Alan Turing Institute | Bayes Centre | Gen AI Lab
School of Mathematics, Gran Sasso Science Institute
email: f dot tudisco at ed.ac.uk
Looking forward for the 28th Biennial Numerical Analysis Conference, admirably organized by my friends and colleagues Alison Ramage, Phil Knight, John Mackenzie from the Math&Stats department at Strathclyde. Have a look at the exciting program and let’s not forget to tweet! #NACONF19
I am organizing a minisymposium and giving a talk:
Minisymposium: Matrix methods for networks
jointly organized with Francesca Arrigo
Abstract: There is a strong relationship between network science and linear algebra, as complex networks can be represented and manipulated using matrices. Some popular tasks in network science, such as ranking nodes, identifying hidden structures, or classifying and labelling components in networks, can be tackled by exploiting the matrix representation of the data. In this minisymposium we sample some recent contributions that build on an algebraic representation of standard and higher-order networks to design models and algorithms to address a diverse range of network problems, including (but not limited to) core-periphery detection and centrality.
Speakers:
My talk will be on Networks core-periphery detection with nonlinear Perron eigenvectors
You may wish to have a look at my slides:
This event is part of the research project MAGNET for which I would like to acknowledge support from the Marie Curie individual fellowship scheme.