Graph algorithms lie at the heart of modern computational theory, probing how networks of vertices and edges can be explored, optimised or transformed under constraints that often defy efficient ...

Graph processing at hyperscale has historically been a challenge because of the sheer complexity of algorithms and graph workflows. Alibaba has been tackling this issue via a project called GraphScope ...

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 machine learning (or graph model), represented by graph neural networks, employs machine learning (especially deep learning) to graph data and is an important research direction in the ...

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 ...

The legendary graph isomorphism problem may be harder than a 2015 result seemed to suggest. “In Laci Babai, you have one of the most legendary and fearsome theoretical computer scientists there ever ...