Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Deep neural models, in recent years, have been successful in almost every field, even
solving the most complex problem statements. However, these models are huge in size with …

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 widely used as a popular representation of the network structure of connected
data. Graph data can be found in a broad spectrum of application domains such as social …

Graphs can model complex relationships between objects, enabling a myriad of Web
applications such as online page/article classification and social recommendation. While …

AI is remarkably successful and outperforms human experts in certain tasks, even in
complex domains such as medicine. Humans on the other hand are experts at multi-modal …

To solve the information explosion problem and enhance user experience in various online
applications, recommender systems have been developed to model users' preferences …

Graphs naturally appear in numerous application domains, ranging from social analysis,
bioinformatics to computer vision. The unique capability of graphs enables capturing the …

Smart contract vulnerability detection draws extensive attention in recent years due to the
substantial losses caused by hacker attacks. Existing efforts for contract security analysis …

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