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

The great losses caused by financial fraud have attracted continuous attention from
academia, industry, and regulatory agencies. More concerning, the ongoing coronavirus …

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 …

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

With the rapid growth of financial services, fraud detection has been a very important
problem to guarantee a healthy environment for both users and providers. Conventional …

With the prevalence of mobile e-commerce nowadays, a new type of recommendation
services, called intent recommendation, is widely used in many mobile e-commerce Apps …

Deep neural networks (DNNs) have been widely applied to various applications, including
image classification, text generation, audio recognition, and graph data analysis. However …

With multiple components and relations, financial data are often presented as graph data,
since it could represent both the individual features and the complicated relations. Due to …

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

Fraud exists in all walks of life and detecting and preventing fraud represents an important
research question relevant to many stakeholders in society. With the rise in big data and …