Lectures related to Strongly regular graph

Explores the increment theorem and derivatives in calculus, focusing on polygonal valleys and curve traces.

Discusses finding d-regular graphs with specific eigenvalue properties and the existence of Ramasugan sequences for d-opt prime numbers.

Covers indexing techniques, distributed retrieval algorithms, and challenges in large-scale web indexing.

Explores adjacency lists, locality, similarity, and efficient representation in link indexing.

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

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

Explores Laplacian matrices of graphs, graph-digraph association, and physical system examples.

Explores link-based ranking and graph mining techniques for efficient web graph analysis.

Explores consensus in networked control systems through graph weight design and matrix properties.

Introduces hypergraphs, generalizing graphs by allowing subsets of nodes to form edges and exploring their applications in various fields.

Explores optimal binary search trees to minimize expected search cost and discusses graphs representation using adjacency matrices and lists.

Covers the concept of adjacency matrix and adjacency lists for representing networks.

Explores consensus algorithms in digraphs, focusing on scenarios where the digraph is not strongly connected.

Introduces elementary graph algorithms, focusing on Breadth-First Search and Depth-First Search.

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

Explores link-based ranking through PageRank and HITS algorithms, covering practical examples and challenges in web search and ranking methods.

Explores DFS, Topological Sort, SCC in graphs, and introduces Flow Networks with practical examples.

Covers the fundamentals and algorithms of link-based ranking, including anchor text indexing, PageRank, HITS, and practical implementations.

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

Explores automorphism groups of trees and graphs, including actions on trees and group homomorphisms.