In a network, pairs of individual elements, or nodes, connect to each other; those connections can represent a sprawling system with myriad individual links. A hypergraph goes deeper: It gives ...

Each of these models would be developed in SystemC. Most of these hardware peripherals would be respective algorithm implementation, for example up samplers, down samplers, data descrambling, crypto ...

Methods: To address these limitations, this study introduces a Graph Attention Network (GAT ... a classical machine learning algorithm, which had an accuracy of 0.70, precision of 0.66, and recall of ...

This project is an interactive A (A-star) pathfinding algorithm visualizer* built with React.js. The A* algorithm is a powerful graph traversal and search algorithm used in game development, robotics, ...

Neo4j Spatial is a library of utilities for Neo4j that faciliates the enabling of spatial operations on data. In particular you can add spatial indexes to already located data, and perform spatial ...

Handling negative cycles in graph algorithms involves detecting them using methods like the Bellman-Ford algorithm and then addressing or avoiding them based on the application. For example ...

In this paper, we develop a novel cell-graph based Cell-Graph Attention (CGAT ... A 5-fold cross-validation study further reduces the number of training examples to about 12 or 13 for each unique ...

Korean research institute Kaist has found a way to develop a one trillion edge graph algorithm on a single computer without storing the graph in the main memory or on disc. ‘Develop’ is the important ...