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

Representation learning in heterogeneous graphs aims to pursue a meaningful vector
representation for each node so as to facilitate downstream applications such as link …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Graphs are data structures that effectively represent relational data in the real world. Graph
representation learning is a significant task since it could facilitate various downstream …

Graph neural networks (GNNs) have been widely used in deep learning on graphs. They
can learn effective node representations that achieve superior performances in graph …

Link prediction aims at inferring missing links or predicting future ones based on the
currently observed network. This topic is important for many applications such as social …

As urban crimes (eg, burglary and robbery) negatively impact our everyday life and must be
addressed in a timely manner, predicting crime occurrences is of great importance for public …

Default user detection plays one of the backbones in credit risk forecasting and
management. It aims at, given a set of corresponding features, eg, patterns extracted from …

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 …

Representation learning in heterogeneous networks faces challenges due to
heterogeneous structural information of multiple types of nodes and relations, and also due …