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

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

Future directions include expanding training to cross-species data sets, integrating PTM and protein–protein interaction data, exploring SE-equivariant GNNs, and optimizing computational efficiency ...

Unsupervised graph-structure learning (GSL) which aims to learn an effective graph structure applied to arbitrary downstream tasks by data itself without any labels’ guidance, has recently received ...

Graph neural network is a new neural network model in recent years, whose advantage lies in processing graph structure data. In the era of big data, people can collect a large amount of graph data for ...

Google AI Mode now supports interactive graphs for financial data right in Search, and it’s a game-changing feature.

Real-world studies, which include a broader population of patients than clinical trials and collect data over longer periods, have provided important information on the clinical course of IPF and ...