Abstract: Graph unlearning has emerged as a pivotal method to delete information from an already trained graph neural network (GNN). One may delete nodes, a class of nodes, edges, or a class of edges.

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 ...

Test your physics knowledge with **“Interactive Physics Quiz: Newton's Second Law Graphing.”** In this video, we guide you through an engaging quiz focused on Newton’s Second Law, helping you ...

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

Abstract: Recently, there has been a surge of interest in Large Language Models (LLMs), this has been accompanied by notable advances in knowledge graphs for text information extraction and database ...