Graph out-of-distribution (OOD) generalization remains a major challenge in graph neural networks (GNNs). Invariant learning, aiming to extract invariant features across varied distributions, has ...

Most security incidents happen in the gap between knowing what matters and actually implementing security controls ...

Text mining and knowledge graphs connect cell-culture parameters to glycosylation for faster bioprocess optimization.

A decentralized cloud security framework uses attribute-based encryption to enable fine-grained access control without centralized vulnerabilities. By combining cryptographic policy enforcement, third ...

Aiomics announces the integration of a Hybrid GraphRAG engine into its clinical platform. By anchoring artificial ...

Keyrus and Veezoo Announce European Strategic Alliance to Accelerate the Adoption of Trusted Agentic Analytics ...

Knowledge graphs are a powerful tool for bringing together information from biological databases and linking what is already known about genes, diseases, treatments, molecular pathways and symptoms in ...

Abstract: Knowledge graphs (KGs), representing multi-relational data as a semantic graph from structured information stored in triples, have attracted wide attention in industrial and academic ...

Abstract: Large Language Models (LLMs) excel at general-purpose reasoning by leveraging broad commonsense knowledge, but they remain limited in tasks requiring personalized reasoning over ...

The figure depicts the four-step，Graph-based Retrieval - Augmented Generation (RAG) process for the RSA - KG system, which aims to integrate multimodal data for RSA diagnosis and treatment. Recurrent ...

What if you could transform overwhelming, disconnected datasets into a living, breathing map of relationships, one that not only organizes your data but also reveals insights you didn’t even know you ...