Lecture

Graph Coloring: Theory and Applications

Related lectures (32)

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

Explores advanced graph coloring concepts, including planted coloring, rigidity threshold, and frozen variables in BP fixed points.

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

Statistical Analysis of Network Data: Structures and Models

Explores statistical analysis of network data, covering graph structures, models, statistics, and sampling methods.

Belief Propagation

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

Handling Networks: Graph Theory

Covers the fundamentals of handling networks and centrality measures in graph theory.

Handling Network Data

Explores handling network data, including types of graphs, real-world network properties, and node importance measurement.

Markov Chains: Applications and Analysis

Explores Markov chains, focusing on the coloring problem and algorithm analysis.

Statistical Analysis of Network Data: Noisy Sampled Networks

Explores statistical analysis of network data, covering noisy sampled networks, likelihood estimation, multilayer networks, and directed networks.

Fixed Points in Graph Theory

Focuses on fixed points in graph theory and their implications in algorithms and analysis.

Statistical analysis of network data

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

Graph Coloring III

Explores properties of clusters and colorability threshold in graph coloring, including average connectivity and rigidity.

Graph Mining: Modularity and Community Detection

Explores community detection in graphs using modularity and edge betweenness.

Graph Coloring: Random vs Symmetrical

Compares random and symmetrical graph coloring in terms of cluster colorability and equilibrium.

Algorithmic Paradigms for Dynamic Graph Problems

Covers algorithmic paradigms for dynamic graph problems, including dynamic connectivity, expander decomposition, and local clustering, breaking barriers in k-vertex connectivity problems.

Epidemic Spreading Models

Covers classical models of epidemic spreading and dynamics on networks with examples.

Graph Algorithms: Modeling and Traversal

Covers graph algorithms, modeling relationships between objects, and traversal techniques like BFS and DFS.

Social and Information Networks: Structure

Explores the structure of social and information networks, focusing on giant components, clustering, tie formation, and network connectivity.

Independence Polynomial of Dependency Graph

Covers the independence polynomial of a dependency graph and related concepts such as graph coloring and directed graph properties.

Belief Propagation for Graph Coloring

Explores Belief Propagation for graph coloring and its convergence properties.