Let G be a graph, A(G) be the adjacency matrix of G, and λ(G) the least eigenvalue of A(G). Information is given about the following three quantities: $\lambda_R(G ...

This project provides a simple implementation of a weighted undirected graph using adjacency linked lists, designed to efficiently handle sparse graphs. It includes several classic graph algorithms ...

Abstract: Computing the product of the (binary) adjacency matrix of a large graph with a real-valued vector is an important operation that lies at the heart of various graph analysis tasks, such as ...

Abstract: Graph clustering is a core technique for network analysis problems, e.g., community detection. This work puts forth a node clustering approach for largely incomplete adjacency graphs. Under ...

Carpathian Journal of Mathematics, Vol. 39, No. 1 (2023), pp. 213-230 (18 pages) The normalized distance Laplacian matrix of a connected graph G, denoted by D𝓛(G), is defined by D𝓛(G) = ...

This project is an implementation of several fundamental graph traversal and pathfinding algorithms in Java. It was completed as part of the CS 314: Data Structures course at The University of Texas ...