As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

Scientist Yi Nian is sharing his machine-learning expertise with the world in his latest co-authored publication, “Globally Interpretable Graph Learning via Distribution Matching.” SEATTLE, Wash. - ...

PARIS, Oct. 28, 2021 – Pasqal, developers of neutral atom-based quantum technology, today announced the publication of a scientific paper in the peer-reviewed APS Physics journal Physical Review A ...

TigerGraph, provider of a leading graph analytics platform, is introducing the TigerGraph ML (Machine Learning) Workbench—a powerful toolkit that enables data scientists to significantly improve ML ...

Data models and query languages are admittedly somewhat dry topics for people who are not in the inner circle of connoisseurs. Although graph data models and query languages are no exception to that ...

If you rotate an image of a molecular structure, a human can tell the rotated image is still the same molecule, but a machine-learning model might think it is a new data point. In computer science ...

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 and ...

Graph-based machine learning is the next wave of digital disruption Machine learning has been getting a lot of attention over the last year or so. This is partly because a variety of companies have ...