Random walk models in the life sciences: including births, deaths and local interactions
Plank, M Simpson, M Baker, R Journal of the Royal Society Interface volume 22 issue 222 (15 Jan 2025)
Tue, 05 Nov 2024
14:00
L5

María Reboredo Prado: Webs in the Wind: A Network Exploration of the Polar Vortex

María Reboredo Prado
(Mathematical Institute)
Abstract

All atmospheric phenomena, from daily weather patterns to the global climate system, are invariably influenced by atmospheric flow. Despite its importance, its complex behaviour makes extracting informative features from its dynamics challenging. In this talk, I will present a network-based approach to explore relationships between different flow structures. Using three phenomenon- and model-independent methods, we will investigate coherence patterns, vortical interactions, and Lagrangian coherent structures in an idealised model of the Northern Hemisphere stratospheric polar vortex. I will argue that networks built from fluid data retain essential information about the system's dynamics, allowing us to reveal the underlying interaction patterns straightforwardly and offering a fresh perspective on atmospheric behaviour.

Tue, 19 Nov 2024
14:00
L5

Brennan Klein: Network Comparison and Graph Distances: A Primer and Open Questions

Brennan Klein
(Northeastern University Network Science Institute)
Further Information

Brennan Klein is an associate research scientist at the Network Science Institute at Northeastern University, where he studies complex systems across nature and society using tools from network science and statistics. His research sits in two broad areas: First, he develops methods and theory for constructing, reconstructing, and comparing complex networks based on concepts from information theory and random graphs. Second, he uses an array of interdisciplinary approaches to document—and combat—emergent or systemic disparities across society, especially as they relate to public health and public safety. In addition to his role at Northeastern University, Brennan is the inaugural Data for Justice Fellow at the Institute on Policing, Incarceration, and Public Safety in the Hutchins Center for African and African American Studies at Harvard University. Brennan received a PhD in Network Science from Northeastern University in 2020 and a B.A. in Cognitive Science from Swarthmore College in 2014. Website: brennanklein.com. Contact: @email; @jkbren.bsky.social.

Abstract
Quantifying dissimilarities between pairs of networks is a challenging and, at times, ill-posed problem. Nevertheless, we often need to compare the structural or functional differences between complex systems across a range of disciplines, from biology to sociology. These techniques form a family of graph distance measures, and over the last few decades, the number and sophistication of these techniques have increased drastically. In this talk, I offer a framework for categorizing and benchmarking graph distance measures in general: the within-ensemble graph distance (WEGD), a measure that leverages known properties of random graphs to evaluate the effectiveness of a given distance measure. In doing so, I hope to offer an invitation for the development of new graph distances, which have the potential to be more informative and more efficient than the tools we have today. I close by offering a roadmap for identifying and addressing open problems in the world of graph distance measures, with applications in neuroscience, material design, and infrastructure networks.
The Blowup Formula for the Instanton Part of Vafa-Witten Invariants on
Projective Surfaces
Kuhn, N Leigh, O Tanaka, Y (25 May 2022) http://arxiv.org/abs/2205.12953v1
Subscribe to