I co-created Graph Neural Networks while at Stanford. I recognized early on that this technology was incredibly powerful.

GDS 2.0 and AuraDS from Neo4j bring graph data science one step closer to mainstream adoption.

When enhanced by the rich, self-describing nature of semantic knowledge graphs, data mesh and data fabric can greatly complement one another.

Companies traveling the long road to becoming data-driven organizations should take a close look at why Graph Databases are taking master data management to a new level.

A sui generis, multi-model open source database, designed from the ground up to be distributed. ArangoDB keeps up with the times and uses graph, and machine learning, as the entry points for its ...

AWS, Google, Neo4j, Oracle. These were just some of the vendors represented in the W3C workshop on web standardization for graph data, and what transcribed is bound to boost adoption of the ...

There is a third way to create advanced data-driven apps and that is to be inspired by products but assemble a solution that imitates a pattern shown by a product. This approach has significant ...

The "Graph Item Type" does not default to "AREA", so be sure to select that for a traditional graph that looks like a rolling hill of data. It's safe to leave "Consolidation Function" to AVERAGE, and ...