I co-created Graph Neural Networks while at Stanford. I recognized early on that this technology was incredibly powerful. Every data point, every observation, every piece of knowledge doesn’t exist in ...

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

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 ...

The addition of vectors provides context to the graph database for enhanced search and supports generative AI and large language models.

As data complexity continues to grow and the demand for real-time insights increases, the move away from traditional relational databases and towards the adoption of graph databases will become vital.

A knowledge graph, is a graph that depicts the relationship between real-world entities, such as objects, events, situations, and concepts. This information is typically stored in a graph database ...

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).

Learn how GraphRAG transforms unstructured text into structured data, revolutionizing AI retrieval with deeper insights and connections.