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, ...
Security Boulevard

Cut your coding agent’s cost with Sonar Vortex

New benchmarks show semantic code graphs helping coding agents find change locations faster and complete updates more ...

Modern Data & Knowledge Platforms: The Foundation Every AI Strategy Actually Runs On: SD Times 100

Part of the SD Times 100 2026 series. See the full SD Times 100 2026 list for every category and honoree. Every conversation ...
IEEE

TCR-pMHC Binding Specificity Prediction From Structure Using Graph Neural Networks

Abstract: The mapping of T-cell-receptors (TCRs) to their cognate peptides is crucial to improving cancer immunotherapy. Numerous computational methods and machine learning tools have been proposed to ...