Lectures related to Decentralized Systems: Gossip Protocol

Graph Algorithms II: Traversal and Paths

Explores graph traversal methods, spanning trees, and shortest paths using BFS and DFS.

Gossip Efficiency: Decentralized Systems

Explores gossip efficiency in decentralized systems, covering protocols, interaction needs, and bandwidth optimization, along with search algorithms and optimizations.

Evolution of Usenet: From ARPANet to Commercial Spam

Explores the evolution of Usenet from ARPANet to commercial spam in the mid-1990s.

Graph Algorithms: Modeling and Traversal

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

Minimum Spanning Trees: Prim's Algorithm

Explores Prim's algorithm for minimum spanning trees and introduces the Traveling Salesman Problem.

Graph Algorithms: Basics

Introduces the basics of graph algorithms, covering traversal, representation, and data structures for BFS and DFS.

Fixed Points in Graph Theory

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

Graph Algorithms: Modeling and Representation

Covers the basics of graph algorithms, focusing on modeling and representation of graphs in memory.

Graph Coloring: Random vs Symmetrical

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

Shortest Path Algorithms: BFS and Dijkstra

Explores Breadth-First Search and Dijkstra's algorithm for finding shortest paths in graphs.

Graphs: Properties and Representations

Covers graph properties, representations, and traversal algorithms using BFS and DFS.

Information Theory: Basics

Covers the basics of information theory, entropy, and fixed points in graph colorings and the Ising model.

Graph Coloring: Theory and Applications

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

Graphical Models: Representing Probabilistic Distributions

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Graph Coloring: Basics and Applications

Covers the basics and applications of graph coloring, including balancing vectors and achieving perfect fairness.

Belief Propagation for Graph Coloring

Explores Belief Propagation for graph coloring and its convergence properties.

Graph Algorithms: BFS and DFS

Explores graph algorithms like BFS and DFS, discussing shortest paths, spanning trees, and data structures' role.

Markov Chains: Applications and Analysis

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

Bellman Ford Algorithm

Explores the Bellman Ford algorithm for finding the shortest path in graphs with negative edge weights.

Graph Algorithms: Memory Management and Traversal

Explores memory management, graph representation, and traversal algorithms in Python, emphasizing BFS and DFS.