Seminars are given every Friday from 2:00 to 3:00 (usually in room 178, but not for the time being). You are welcome 10 minutes earlier for tea, coffee and cake.

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Next talks:

The online talks are available here :

Vendredi 23 septembre à 14h - Michael Savery
Induced subgraphs of induced subragphs of large chromatic number

What structure can be found in a graph G with large chromatic number? One possibility is that G contains a large clique, but if it does not, then can we find an induced subgraph of G which still has fairly large chromatic number but is simpler than G in some way? In this talk we will discuss some limitations of this approach. We will prove that for every graph F with at least one edge there are graphs G of arbitrarily large chromatic number and the same clique number as F such that every induced subgraph of G with chromatic number at least c = c(F) contains an induced copy of F. This generalises recent theorems of Briański, Davies, and Walczak, and of Carbonero, Hompe, Moore, and Spirkl. We will also outline how to show an analogous statement where clique number is replaced by odd girth. This is joint work with António Girão, Freddie Illingworth, Emil Powierski, Alex Scott, Youri Tamitegama, and Jane Tan.

Vendredi 9 septembre à 14h - Jane Tan
Exploring comparable box dimension

Two boxes in R^d are comparable if one of them is a subset of a translation of the other. The comparable box dimension of a graph G is the minimum d such that G can be represented as a touching graph of comparable axis-aligned boxes in R^d. Having finite comparable box dimension implies a number of nice graph properties, which leads us to consider which graphs have such geometric representations. In this talk, we show that comparable box representations behave well under several common operations on graphs. This leads to a proof that every proper minor-closed class of graphs has finite comparable box dimension. Based on joint work with Zdeněk Dvořák, Daniel Goncalves, Abhiruk Lahiri and Torsten Ueckerdt.



GT Graphs and Optimization


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