Combinatorics

Combinatorics is a fundamental mathematical discipline which focuses on the study of discrete objects and their properties. The current workshop brought together researchers from diverse fields such as Extremal and Probabilistic Combinatorics, Discrete Geometry, Graph theory, Combiantorial Optimization and Algebraic Combinatorics for a fruitful interaction. New results, methods and developments and future challenges were discussed. This is a report on the meeting containing abstracts of the presentations and a summary of the problem session

The giant component of the directed configuration model revisited

We prove a law of large numbers for the order and size of the largest strongly connected component in the directed configuration model. Our result extends previous work by Cooper and Frieze (2004).Supported by the Spanish Ministerio de Economía y Competitividad project MTM2017- 82166-P and the MSCA-RISE-2020-101007705 - ’RandNet’. 1517Postprint (author's final draft

Threshold phenomena involving the connected components of random graphs and digraphs

We consider some models of random graphs and directed graphs and investigate their behavior near thresholds for the appearance of certain types of connected components. Firstly, we look at the critical window for the appearance of a giant strongly connected component in binomial random digraphs. We provide bounds on the probability that the largest strongly connected component is very large or very small. Next, we study the configuration model for graphs and show new upper bounds on the size of the largest connected component in the subcritical and barely subcritical regimes. We also show that these bounds are tight in some instances. Finally we look at the configuration model for random digraphs. We investigate the barely sub-critical region and show that this model behaves similarly to the binomial random digraph whose barely sub- and super-critical behaviour was studied by Luczak and Seierstad. Moreover, we show the existence of a threshold for the existence of a giant weak component, as predicted by Kryven.En aquesta tesi considerem diversos models de grafs i graf dirigits aleatoris, i investiguem el seu comportament a prop dels llindars per l'aparició de certs tipus de components connexes. En primer lloc, estudiem la finestra crítica per a l'aparició d'una component fortament connexa en dígrafs aleatoris binomials (o d'Erdos-Rényi). En particular, provem diversos resultats sobre la probabilitat límit que la component fortament connexa sigui sigui molt gran o molt petita. A continuació, estudiem el model de configuració per a grafs no dirigits i mostrem noves cotes superiors per la mida de la component connexa més gran en els règims sub-crítics i quasi-subcrítics. També demostrem que, en general, aquestes cotes no poden ser millorades. Finalment, estudiem el model de configuració per a dígrafs aleatoris. Ens centrem en la regió quasi-subcrítica i demostrem que aquest model es comporta de manera similar al model binomial, el comportament del qual va ser estudiat per Luczak i Seierstad en les regions quasi-subcrítica i quasi-supercrítica. A més a més, demostrem l'existència d'una funció llindar per a l'existència d'una component feble gegant, tal com va predir Kryven.Postprint (published version

On Defeating Graph Analysis of Anonymous Transactions

In a ring-signature-based anonymous cryptocurrency, signers of a transaction are hidden among a set of potential signers, called a ring, whose size is much smaller than the number of all users. The ring-membership relations specified by the sets of transactions thus induce bipartite transaction graphs, whose distribution is in turn induced by the ring sampler underlying the cryptocurrency. Since efficient graph analysis could be performed on transaction graphs to potentially deanonymise signers, it is crucial to understand the resistance of (the transaction graphs induced by) a ring sampler against graph analysis. Of particular interest is the class of partitioning ring samplers. Although previous works showed that they provide almost optimal local anonymity, their resistance against global, e.g. graph-based, attacks were unclear. In this work, we analyse transaction graphs induced by partitioning ring samplers. Specifically, we show (partly analytically and partly empirically) that, somewhat surprisingly, by setting the ring size to be at least logarithmic in the number of users, a graph-analysing adversary is no better than the one that performs random guessing in deanonymisation up to constant factor of 2