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

Our proposed framework (Figure 1) embeds protein sequences using ESM-2 and converts predicted structures into Node2Vec-encoded contact graphs. (24) These representations are processed via dual ...

Graph neural networks (GNNs) have achieved considerable success in dealing with graph-structured data by the message-passing mechanism. Actually, this mechanism relies on a fundamental assumption that ...

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 ...

Key U.S. Senator Tells White House Crypto Market Structure Bill Will Be Done by Sept. 30 The Senate and House are sending mixed messages on the most important crypto legislation awaited by the ...

Spending time as wee hackers perusing the family atlas taught us an appreciation for a good map, and [Billy Roberts], a cartographer at NREL, has served up a doozy with a map of the data center inf… ...

BingoCGN, a scalable and efficient graph neural network accelerator that enables inference of real-time, large-scale graphs through graph partitioning, has been developed by researchers at ...

Unlike traditional databases, knowledge graphs organize information as nodes and edges, making them better for AI systems that reason & infer.

The U.S. Congress is in the thick of its crypto efforts this week, with the Senate starting on final votes to approve its first-ever crypto bill.