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

With the rapid development of the World Wide Web (WWW), heterogeneous graphs (HG)
have explosive growth. Recently, heterogeneous graph neural network (HGNN) has shown …

Heterogeneous Graph Neural Networks (HGNNs), as a kind of powerful graph
representation learning methods on heterogeneous graphs, have attracted increasing …

The timely prediction of bankruptcy is highly desirable to guarantee an upward spiral for
overall societal well-being. By extracting multifaceted information from the business …

Abstract In recent years, Graph Neural Networks (GNNs), an emerging technology for
learning from graph-structured data, have attracted much attention. Despite the widespread …

Heterogeneous graphs are ubiquitous to model complex data. There are urgent needs on
powerful heterogeneous graph neural networks to effectively support important applications …

Abstract Heterogeneous Graph Neural Networks (HGNNs) have demonstrated significant
success in capturing complex interactions within heterogeneous graphs to learn graph …

CAD assembly modeling, which refers to using CAD software to design new products from a
catalog of existing machine components, is important in the industrial field. The graph neural …

On graph data, the multitude of node or edge types gives rise to heterogeneous information
networks (HINs). To preserve the heterogeneous semantics on HINs, the rich node/edge …

The representation of heterogeneous graph nodes has become a hot research topic due to
its diverse applications. However, extant approaches can only give consideration partly to …