Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in
naturally modeling a broad range of systems where high-order relationships exist among …

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 …

Heterogeneous graph neural networks (HGNNs) have the powerful capability to embed rich
structural and semantic information of a heterogeneous graph into node representations …

Generative self-supervised learning (SSL), especially masked autoencoders, has become
one of the most exciting learning paradigms and has shown great potential in handling …

The advancement in internet technology has enabled the use of increasingly sophisticated
data by recommendation systems to enhance their effectiveness. This data is comprised of …

Recent studies have highlighted the limitations of message-passing based graph neural
networks (GNNs), eg, limited model expressiveness, over-smoothing, over-squashing, etc …

Abstract Graph Neural Networks (GNNs) have achieved excellent performance of graph
representation learning and attracted plenty of attentions in recent years. Most of GNNs aim …

Heterogeneous information network embedding aims to learn low-dimensional node vectors
in heterogeneous information networks (HINs), concerning not only structural information but …

Graph neural networks (GNNs) aim to learn well-trained representations in a lower-
dimension space for downstream tasks while preserving the topological structures. In recent …

The domain of Machine learning has experienced Substantial advancement and
development. Recently, showcasing a Broad spectrum of uses like Computational …