Heterogeneous information networks (HIN) have gained increasing popularity in recent
years for capturing complex relations between diverse types of nodes. Meta-structures are …

Existing heterogeneous graph neural networks (HGNNs) have achieved great success in
utilizing the rich semantic information in heterogeneous information networks (HINs) …

In recent years, Heterogeneous Graph Neural Networks (HGNNs) have been the state-of-the-
art approaches for various tasks on Heterogeneous Graphs (HGs), eg, recommendation and …

Heterogeneous graph data are ubiquitous and extracting information from them is
increasingly crucial. Existing approaches for modeling heterogeneous graphs often rely on …

Existing graph learning-based cognitive diagnosis (CD) methods have made relatively good
results, but their student, exercise, and concept representations are learned and exchanged …

Heterogeneous graphs have attracted considerable research interests in the past few years
owing to their remarkable ability to represent complex real-world systems. However, the …

Heterogeneous graph neural architecture search (HGNAS) represents a powerful tool for
automatically designing effective heterogeneous graph neural networks. However, existing …

Utilizing long-range dependency, a concept extensively studied in homogeneous graphs,
remains underexplored in heterogeneous graphs, especially on large ones, posing two …

In recent years, heterogeneous graphs, a complex graph structure that can express multiple
types of nodes and edges, have been widely used for modeling various real-world …

Social media data is inherently rich, as it includes not only text content, but also users,
geolocation, entities, temporal information, and their relationships. This data richness can be …