Convolutional neural networks (CNNs) have received widespread attention due to their
powerful modeling capabilities and have been successfully applied in natural language …

Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space,
perform particularly well in various tasks that process graph structure data. With the rapid …

Abstract Accurate Link Prediction (LP) in Social Networks (SNs) is crucial for various
practical applications, such as recommendation systems and network security. However …

Patients with the same cancer types may present different genomic features and therefore
have different drug sensitivities. Accordingly, correctly predicting patients' responses to the …

Real-world objects are usually defined in terms of their own relationships or connections. A
graph (or network) naturally expresses this model though nodes and edges. In biology …

Subgraph-based methods have proven to be effective and interpretable in predicting drug-
drug interactions (DDIs), which are essential for medical practice and drug development …

Advanced computational methods are being actively sought to address the challenges
associated with the discovery and development of new combinatorial materials, such as …

Abstract Machine learning models for exploring structure-property relation for hydroxyapatite
nanoparticles (HANPs) are still lacking. A multiscale multisource dataset is presented …

Mission-critical embedded software is critical to our society's infrastructure but can be
subject to new security vulnerabilities as technology advances. When security issues arise …

Structured entities analysis is the basis of the modern science, such as chemical science,
biological science, environmental science and medical science. Recently, a huge amount of …