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 ...

Some applications are so inherently complicated that it is difficult to dig through the many layers of connected algorithms to expose the parts of the code ripe for optimization. This makes them a ...

Efficiently and quickly chewing through one trillion edges of a complex graph is no longer in itself a standalone achievement, but doing so on a single node, albeit with some acceleration and ...

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 ...

What Is a Graph Database? Your email has been sent Explore the concept of graph databases, their use cases, benefits, drawbacks, and popular tools. A graph database is a dynamic database management ...

Debate and discussion around data management, analytics, BI and information governance. This is a guest blogpost by Amy Holder from Neo4j. She examines recent interest in graph databases as the basis ...