The precise prediction of molecular properties is essential for advancements in drug
development, particularly in virtual screening and compound optimization. The recent …

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

Graph neural networks (GNNs) are commonly used and have shown promising performance
in recommendation systems. A major branch, heterogeneous GNNs, models heterogeneous …

Molecular property prediction faces the challenge of limited labeled data as it necessitates a
series of specialized experiments to annotate target molecules. Data augmentation …

Self-supervised learning (SSL) has recently achieved great success in mining the user-item
interactions for collaborative filtering. As a major paradigm, contrastive learning (CL) based …

Graph neural networks (GNNs) are powerful models for processing graph data and have
demonstrated state-of-the-art performance on many downstream tasks. However, existing …

Malicious social bots achieve their malicious purposes by spreading misinformation and
inciting social public opinion, seriously endangering social security, making their detection a …

Using machine learning (ML) techniques to predict material properties is a crucial research
topic. These properties depend on numerical data and semantic factors. Due to the …

Molecule generation is a critical process in the fields of drug discovery and materials
science. Recently, generative models based on normalizing flows have demonstrated …

In recent years, graph neural network technology has revolutionized molecular graph
matching methods, offering new opportunities for drug discovery. Central to this research are …