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

Dynamic graph algorithms and data structures represent a vital research frontier in computer science, underpinning applications from network analysis to real-time system monitoring.

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 ...

Description The design, implementation, and analysis of abstract data types, data structures and their algorithms. Topics include: data and procedural abstraction, amortized data structures, trees and ...

Neo4j, a provider of graph technology, is launching Neo4j for Graph Data Science, a data science environment built to harness the predictive power of relationships for enterprise deployments. Neo4j ...

The rise of graph databases is closely related to AI's demand for data processing. AI technology requires vast amounts of structured and unstructured data, which must not only be input into ...