The increasing reliance on knowledge graphs parallels that of Artificial Intelligence for three irrefutable reasons. They’re the most effective means of preparing data for statistical AI, creditable ...

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 ...

This research topic explores the theoretical foundations and practical applications of graph labeling and coloring problems, both of which are central to modern combinatorics and computer science.

Imagine a world where artificial intelligence not only understands language but creates with it, where quantum systems no longer feel like an enigma but a solvable puzzle. It might sound like science ...

From powering search engines to securing data and optimizing networks, algorithms underpin nearly every aspect of modern technology. Understanding how efficiently they can solve problems — and where ...