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. These methods ...

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

Just getting data in and out of property graph solutions is an exercise in patience and improvisation -- good luck representing a graph structure in CSV, and mapping that from solution to solution.

Meiya Pico's patent focuses on classifying social text using Graph Convolutional Networks. GCN is a deep learning model ...

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 ...

Soren Lassen, manager of the search infrastructure team at Facebook, described that "in computer science speak," the Social Graph is a graph data structure involving an "immense map" of people ...