Graph Coloring: Random vs Symmetrical

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Message passing in graphical models

Explains message passing in graphical models and the matching problem in graph theory.

Matrices and Networks

Explores the application of matrices and eigendecompositions in networks.

Graph Theory and Network Flows

Introduces graph theory, network flows, and flow conservation laws with practical examples and theorems.

Graph Theory Basics

Introduces graph theory basics, Ramsey theory, and graph coloring concepts.

Fixed Points in Graph Theory

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

Dynamic Programming: Knapsack

Explores dynamic programming for the Knapsack problem, discussing strategies, algorithms, NP-hardness, and time complexity analysis.

Interlacing Families and Ramanujan Graphs

Explores interlacing families of polynomials and 1-sided Ramanujan graphs, focusing on their properties and construction methods.

Markov Chains and Algorithm Applications

Covers the fundamentals of Markov chains and their applications in algorithms, focusing on proper coloring and the Metropolis algorithm.

Quantifying Randomness and Information in Biological Data

Explores entropy, randomness, and information quantification in biological data analysis, including neuroscience and protein structure prediction.

Network clustering

Explores network clustering, spectral clustering, k-means algorithm, eigenvalue properties, block model estimation, and structural similarity measurement.