Graph machine learning (or graph model), represented by graph neural networks, employs machine learning (especially deep learning) to graph data and is an important research direction in the ...

Complex problem solving (CPS) represents an essential dimension of contemporary education, capturing the multifaceted cognitive and metacognitive processes that underpin effective learning in ...

A startup named TigerGraph emerged from stealth today with a new native parallel graph database that its founder thinks can shake up the analytics market. With $31 million in venture funding and ...

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 ...