Graph out-of-distribution (OOD) generalization remains a major challenge in graph neural networks (GNNs). Invariant learning, aiming to extract invariant features across varied distributions, has ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models ...

To some, METR’s “time horizon plot” indicates that AI utopia—or apocalypse—is close at hand. The truth is more complicated.

Text mining and knowledge graphs connect cell-culture parameters to glycosylation for faster bioprocess optimization.

Like many of us, [Tim]’s seen online videos of circuit sculptures containing illuminated LED filaments. Unlike most of us, however, he went a step further by using graph theory to design glowing ...

In order to explore the medication rules of Shang Han Lun, this article conducted complex network analysis and cluster ...

Abstract: Graph Neural Networks (GNNs) have gained popularity as an efficient choice for learning on graph-structured data. However, most methods are node or graph-centered, often overlooking valuable ...

According to @godofprompt, leading AI engineers at OpenAI, Anthropic, and Microsoft are shifting from traditional RAG (Retrieval-Augmented Generation) systems to graph-enhanced retrieval methods, ...

According to God of Prompt (@godofprompt), top engineers at AI companies such as OpenAI, Anthropic, and Microsoft are moving beyond basic Retrieval-Augmented Generation (RAG) by prioritizing ...

Due to the complexity of hotel operation processes, abnormal situations are inevitable, making proactive anomaly prediction essential for ensuring operational stability. Although current deep learning ...

Field and Space Experimental Robotics (FASER) Laboratory, Mechanical Engineering Department, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States This article proposes a ...