Complex networks offer a robust framework for understanding language and text as emergent, multi-level systems. Using graph theory, researchers represent words, punctuation, and semantic units as ...

What if you could transform vast amounts of unstructured text into a living, breathing map of knowledge—one that not only organizes information but reveals hidden connections you never knew existed?

Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...

Debate and discussion around data management, analytics, BI and information governance. This is a guest blogpost by Neo4j’s Jim Webber, who says graphs are a way of managing complexity that is all ...

Graph Neural Networks (GNNs) and GraphRAG don’t “reason”—they navigate complex, open-world financial graphs with traceable, multi-hop evidence. Here’s why BFSI leaders should embrace graph-native AI ...