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 ...
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

Rotation- and Permutation-Equivariant Quantum Graph Neural Network for 3D Graph Data

Abstract: Equivariant quantum graph neural networks (EQGNNs) offer a potentially powerful method to process graph data. However, existing EQGNN models only consider the permutation symmetry of graphs, ...

Cochlea network model reveals how inner ear may sort sound from noise

Over 70 million people in the U.S. are impacted by hearing loss, and age-related hearing loss is the second most common ...

Graphene can hold multiple states of superconductivity, a new study finds

The ordinary graphite in pencil lead is proving to be surprisingly multifaceted at the microscale. In a study published in ...