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.

Data-hungry AI applications are fed complex information, and that's where graph databases and knowledge graphs play a crucial role.

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 ...

Graph-structured data are pervasive in the real-world such as social networks, molecular graphs and transaction networks.

Key-value, document-oriented, column family, graph, relational… Today we seem to have as many kinds of databases as there are kinds of data. While this may make choosing a database harder, it ...

The flexibility offered by a knowledge-graph-powered data catalog enables near-immediate support for new types of data sources; a knowledge graph makes it easy to extend the model to represent ...

Graph analytics provide another arrow in our quiver – another tool that we can use against these vast amounts of social media and sensor-based data to uncover new insights about the ...

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 ...

In addition to streamlining how users retrieve diverse data via automation capabilities, a knowledge graph standardizes those data according to relevant business terms and models ...