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

Omnipresent network/graph data generally have the characteristics of nonlinearity,
sparseness, dynamicity and heterogeneity, which bring numerous challenges to network …

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 arise naturally in many real-world applications including social networks,
recommender systems, ontologies, biology, and computational finance. Traditionally …

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 …

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

Heterogeneous information networks usually contain different kinds of nodes and
distinguishing types of relations, which can preserve more information than homogeneous …

Android is undergoing unprecedented malicious threats daily, but the existing methods for
malware detection often fail to cope with evolving camouflage in malware. To address this …

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 …

Understanding the interconnected relationships of large-scale information networks like
social, scholar and Internet of Things networks is vital for tasks like recommendation and …