Concept

Extremal graph theory

Related lectures (17)

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Covers stochastic properties, network structures, models, statistics, centrality measures, and sampling methods in network data analysis.

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Explores Extreme Value Theory applications, estimation strategies, and modelling techniques for statistical analysis of extremes in time series.

Szemerédi Regularity Lemma

Explores the Szemerédi Regularity Lemma, e-regularity in bipartite graphs, supergraph structure, and induction techniques.

Graph Theory Fundamentals

Explores fundamental graph theory concepts, Erdős' results, Chromatic Lemma, and Union Bound theorem in graph theory.

Extreme Value Time Series: Modelling and Dependence

Explores extremal limit theorems, point processes, and multivariate extremes in extreme value time series modelling, emphasizing the effect of local dependence on extreme values.

Cluster Complexity Analysis

Covers the analysis of cluster complexity in random graph coloring.

Cheeger's Inequalities

Covers Cheeger's inequalities and the combinatorial properties of graphs.

Isogenic Graphs: Spectral Analysis and Mathematical Applications

Explores isogenic graphs, spectral properties, and mathematical applications in modular forms and cryptography.

Statistical Physics of Clusters

Explores the statistical physics of clusters, focusing on complexity and equilibrium behavior.

Belief Propagation

Explores Belief Propagation in graphical models, factor graphs, spin glass examples, Boltzmann distributions, and graph coloring properties.

Graph Statistics: Random Graphs, Graph Homomorphisms, and Network Analysis

Explores graph statistics, random graph generation, network analysis, centrality measures, and clustering coefficients.

Spectral Graph Theory: Introduction

Introduces Spectral Graph Theory, exploring eigenvalues and eigenvectors' role in graph properties.

Epidemics and Bootstrap Percolation in Square Lattice

Explores epidemics spread models and Bootstrap Percolation in square lattice networks, focusing on the Kolmogorov equation and probability generating functions.

Graph Coloring: Theory and Applications

Covers the theory and applications of graph coloring, focusing on disassortative stochastic block models and planted coloring.

Regularity Lemmas and Density Theorems

Explores Regularity Lemmas and Density Theorems for graph partitioning and structure identification.

Poisson Process Theory: Properties and Applications

Explores Poisson process theory, covering properties, applications, and key theorems.