Tue, 09 May 2023

14:00 - 15:00
L5

Colouring and domination in tournaments

Paul Seymour
(Princeton)
Abstract

"Colouring" a tournament means partitioning its vertex set into acylic subsets; and the "domination number" is the size of the smallest set of vertices with no common in-neighbour. In some ways these are like the corresponding concepts for graphs, but in some ways they are very different. We give a survey of some recent results and open questions on these topics.

Joint with Tung Nguyen and Alex Scott.

On the mathematical theory of plumes
Garvey, R Fowler, A Geophysical & Astrophysical Fluid Dynamics volume 117 issue 2 79-106 (04 Mar 2023) doi:10.1080/03091929.2023.2187054
Rate-dependent response of axonal microtubules and tau proteins under shear forces
Bellino, L Materials Research Proceedings (MRP) volume 26 65-70 (26 Feb 2023) doi:10.21741/9781644902431-11
Self-consistent models of our Galaxy
Binney, J Vasiliev, E Monthly Notices of the Royal Astronomical Society volume 520 issue 2 1832-1847 (07 Feb 2023) doi:10.1093/mnras/stad094
Dual Bayesian ResNet: A Deep Learning Approach to Heart Murmur Detection
H. Krones, F Walker, B Mahdi, A Kiskin, I Lyons, T Parsons", G Computing in Cardiology Conference (CinC) (03 Apr 2023) doi:10.22489/cinc.2022.355
Tue, 06 Jun 2023
14:00
C6

Dr. Guillaume St-Onge

Dr. Guillaume St-Onge
(Northeastern University Network Science Institute)
Abstract

TBA

Tue, 23 May 2023
14:00
C6

What we do in the shadows: mining temporal motifs from transactions on the Dark Web

Dr. Naomi Arnold
(Northeastern University London)
Abstract
Dark web marketplaces are forums where users can buy or sell illicit goods/services and transactions are typically made using cryptocurrencies. While there have been numerous coordinated shutdowns of individual markets by authorities, the ecosystem has been found to be immensely resilient. In addition, while transactions are open and visible by anyone on the blockchain, the sheer scale of the data makes monitoring beyond basic characteristics a huge effort.

In this talk, I propose the use of temporal motif counting, as a way of monitoring both the system as a whole and the users within it. Focusing on the Alphabay and Hydra dark markets, I study all the motifs formed by three sequential transactions among two to three users, finding that they can tell us something more complex than can be captured by simply degree or transaction volume. Studying motifs local to the node, I show how users form salient clusters, which is a promising route for classification or anomaly detection tasks.
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