HackerNoon

The Race to Build AI’s Context Layer Is Really About Meaning

Context graphs, graph memory, and ontologies for AI are converging. What does this mean for enterprise AI in 2026?
IEEE

Graph Neural Networks for Graphs With Heterophily: A Survey

Abstract: Recent years have witnessed fast developments of graph neural networks (GNNs) that have benefited myriad graph analytic tasks and applications. Most GNNs rely on the homophily assumption ...
techxplore

New graph attention network models higher-order relationships in complex graph data

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...
Quanta Magazine

New Proof Settles Decades-Old Bet About Connected Networks

According to mathematical legend, Peter Sarnak and Noga Alon made a bet about optimal graphs in the late 1980s. They’ve now both been proved wrong. It started with a bet. In the late 1980s, at a ...
IEEE

GIGNet: A Graph-in-Graph Neural Network for Automatic Modulation Recognition

Abstract: In this paper, we propose a robust end-to-end classification model, Graph-in-Graph Neural Network (GIGNet), for automatic modulation recognition (AMR). In GIGNet, multi-level graph neural ...
TechRepublic

Network Definition Made Simple: Here’s the Basics

Spoiler: there isn’t just one network definition. Learn everything you need to assess various network types, topologies, and architectures. The simple network definition: a system that links other ...
Scientific Research Publishing

Prediction of the Price of Advanced Global Stock Markets Using Machine Learning: Comparative Analysis ()

Finance and Business Sector, Institute of Public Administration, Riyadh, Saudi Arabia. This paper seeks to forecast the daily closing prices of advanced global stock markets by employing machine ...