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

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 …

A large number of real-world graphs or networks are inherently heterogeneous, involving a
diversity of node types and relation types. Heterogeneous graph embedding is to embed …

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

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 …

Ride-hailing applications are becoming more and more popular for providing drivers and
passengers with convenient ride services, especially in metropolises like Bei**g or New …

Network embedding, which aims to represent network data in a low-dimensional space, has
been commonly adopted for analyzing heterogeneous information networks (HIN). Although …

As social animals, attending group activities is an indispensable part in people's daily social
life, and it is an important task for recommender systems to suggest satisfying activities to a …

Providing accurate recommendations to newly joined users (or potential users, so-called
cold-start users) has remained a challenging yet important problem in recommender …

Cross-platform account matching plays a significant role in social network analytics, and is
beneficial for a wide range of applications. However, existing methods either heavily rely on …