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 ...

This page assumes you are already familiar with the content of Introduction to visualization; in particular, you should already understand the sequence graph representation used. Right now you may be ...

Abstract: Many real-world networks are characterized by directionality; however, the absence of an appropriate Fourier basis hinders the effective implementation of graph signal processing techniques.

The prediction of the properties of crystal materials has always been a core issue in materials science and solid-state physics. With the rapid development of computer simulation techniques and ...

Professor Edmund Lam, Dr. Ni Chen and their research team from the Department of Electrical and Electronic Engineering under the Faculty of Engineering at the University of Hong Kong (HKU) have ...

In this tutorial, we guide you through the development of an advanced Graph Agent framework, powered by the Google Gemini API. Our goal is to build intelligent, multi-step agents that execute tasks ...

Abstract: Graph signal processing (GSP) is a prominent framework for analyzing signals on non-Euclidean domains. The graph Fourier transform (GFT) uses the combinatorial graph Laplacian matrix to ...

AIs can outperform humans easily on short tasks, but longer ones are the true hurdle to overcome before we can deem them to be truly intelligent systems. When you purchase through links on our site, ...

Mathematicians sometimes think of their research as a garden and unsolved problems as seeds waiting to sprout. Some problems are analogous to tulip bulbs. As mathematicians work to solve them, they ...