Heterogeneous graphs (HGs) also known as heterogeneous information networks have
become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn …

Knowledge graphs (KGs) have rapidly emerged as an important area in AI over the last ten
years. Building on a storied tradition of graphs in the AI community, a KG may be simply …

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

Since real-world objects and their interactions are often multi-modal and multi-typed,
heterogeneous networks have been widely used as a more powerful, realistic, and generic …

Heterogeneous graphs, which consist of multiple types of nodes and edges, are highly
suitable for characterizing real-world complex systems. In recent years, due to their strong …

Heterogeneous graph convolutional networks have gained great popularity in tackling
various network analytical tasks on heterogeneous network data, ranging from link …

Network embedding aims to embed nodes into a low-dimensional space, while capturing
the network structures and properties. Although quite a few promising network embedding …

Heterogeneous information network (HIN) embedding aims at learning the low-dimensional
representation of nodes while preserving structure and semantics in a HIN. Existing methods …

As heterogeneous networks have become increasingly ubiquitous, Heterogeneous
Information Network (HIN) embedding, aiming to project nodes into a low-dimensional …

Graph neural network (GNN), as a powerful graph representation technique based on deep
learning, has shown superior performance and attracted considerable research interest …