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

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

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

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 ...

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

In the new knowledge-based digital world, encoding and making use of business and operational knowledge is the key to making progress and staying competitive. Here's a shortlist of technologies ...

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 graph representation of coauthorship, taken from either data set, might look like a triangle, showing that each mathematician (three nodes) had collaborated with the other two (three links).