Many students appear to be completing assignments faster while learning less from them. This conclusion comes from one of the largest studies of how generative AI is changing student behavior and ...

Last week, OpenAI shocked the mathematical community by revealing that one of its internal artificial intelligence (AI) models had found a counterexample to a famous conjecture made by legendary ...

Graph Neural Networks (GNNs) have gained considerable attention in recent years. Despite the surge in innovative GNN architecture designs, research heavily relies on the same 5-10 benchmark datasets ...

RI is a general purpose algorithm for one-to-one exact subgraph isomorphism problem maintaining topological constraints. It is both a C++ library and a standalone tool, providing developing API and a ...

Abstract: The isomorphism problem, crucial in network analysis, involves analyzing both low-order and high-order structural information. Graph isomorphism algorithms focus on structural equivalence to ...

Abstract: To surpass the limitation of conventional artificial intelligence-enabled automatic design methodology, this work proposes a novel workflow to implement the emerging graph-learning-based ...

Over the past few years, graph neural networks and graph transformers have been successfully used to analyze graph-structured data, mainly focusing on node classification and link prediction tasks.

Mathematicians have long been fascinated by objects that exhibit exceptionally nice combinatorial properties. However, it is often difficult to determine whether objects satisfying a given ...