Abstract: This research explores optimizing recruitment and shortlisting processes using advanced graph algorithms. The study aims to construct and refine a dynamic knowledge graph that integrates ...

Graph sparsification refers to the process of approximating a dense graph by a much sparser one while retaining key structural and spectral properties. In the context of linear systems, and in ...

Graph algorithms for spanning structures encompass a family of techniques designed to extract sparse subgraphs that preserve essential connectivity and distance properties of the original network.

Nearest neighbor search is a fundamental data structure problem with many applications in machine learning, computer vision, recommendation systems and other fields. Although the main objective of the ...

Abstract: Graph algorithms play a pivotal role in solving allocation problems, which are fundamental across diverse domains such as transportation, telecommunications, supply chain management, and ...

Neo4j Inc. today announced a new serverless offering that dramatically simplifies the deployment of its graph database offering, making it easier to use with artificial intelligence applications. Most ...

Graph algorithms are in wide use in DoD software applications, including intelligence analysis, autonomous systems, cyber intelligence and security, and logistics optimizations. These algorithms make ...

Graph theory is an integral component of algorithm design that underlies sparse matrices, relational databases, and networks. Improving the performance of graph algorithms has direct implications to ...