Graph Coloring: Theory and Applications

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (32)

Page 2 of 4

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.