Data-centric AI, with its primary focus on the collection, management, and utilization of data
to drive AI models and applications, has attracted increasing attention in recent years. In this …

Homophily principle, ie, nodes with the same labels are more likely to be connected, has
been believed to be the main reason for the performance superiority of Graph Neural …

Despite advances in the field of Graph Neural Networks (GNNs), only a small number (~ 5)
of datasets are currently used to evaluate new models. This continued reliance on a handful …

Abstract Semi-Supervised Graph Clustering (SSGC) has emerged as a pivotal field at the
intersection of graph clustering and semi-supervised learning (SSL), offering innovative …

Federated Graph Learning (FGL) aims to learn graph learning models over graph data
distributed in multiple data owners, which has been applied in various applications such as …

Data continuously emitted from industrial ecosystems such as social or e-commerce
platforms are commonly represented as heterogeneous graphs (HG) composed of multiple …

Graph neural networks are typically trained on individual datasets, often requiring highly
specialized models and extensive hyperparameter tuning. This dataset-specific approach …

Homophily principle, ie, nodes with the same labels are more likely to be connected, has
been believed to be the main reason for the performance superiority of Graph Neural …

Procedural level generation techniques that learn local neighborhoods from example levels
(such as WaveFunctionCollapse) have risen in popularity. Usually the neighborhood …

Graphs are general data structures that can represent information from a variety of domains
(social, biomedical, online transactions, and many more). Graph Neural Networks (GNNs) …