Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

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 …

Graph neural networks (GNNs) have been proven to be effective in various network-related
tasks. Most existing GNNs usually exploit the low-frequency signals of node features, which …

Abstract Heterogeneous Graph Neural Networks (HGNNs) have drawn increasing attention
in recent years and achieved outstanding performance in many tasks. The success of the …

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 …

Graph Neural Networks (GNNs) have been widely used for the representation learning of
various structured graph data, typically through message passing among nodes by …

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 …

Graphs are widely used to model interconnected entities and improve downstream
predictions in various real-world applications. However, real-world graphs nowadays are …

Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …