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

Graph data science is when you want to answer questions, not just with your data, but with the connections between your data points — that’s the 30-second explanation, according to Alicia Frame.

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

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 ...

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

The bottom line is that “graph databases are a type of NoSQL database that stores data as a network of interconnected nodes and edges,” Nadkarni explained. “They manage complex relationships between ...

Network graphs, now growing in popularity, are designed to help us see connections, capturing the interplay between many dimensions.

Knowledge graphs are well-suited to organizations with large data sets and where extracting knowledge often proves burdensome.

The cross-device company Tapad was an early mover in an ongoing trend where ad tech data companies divest themselves of their media sales businesses. In January 2018, Tapad – owned by the Norwegian ...