Abstract Large Language Models (LLMs), such as ChatGPT and Bard, have revolutionized
natural language understanding and generation. They possess deep language …

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 …

Graph-structured data, prevalent in domains ranging from social networks to biochemical
analysis, serve as the foundation for diverse real-world systems. While graph neural …

The pursuit of more intelligent and credible autonomous systems, akin to human society, has
been a long-standing endeavor for humans. Leveraging the exceptional reasoning and …

Ultrasound is a critical imaging technique for diagnosing breast cancer. However, the
multimodal breast ultrasound diagnostic process is time-consuming and labor-intensive …

Drug-drug interaction (DDI) is the combined effects of multiple drugs taken together, which
can either enhance or reduce each other's efficacy. Thus, drug interaction analysis plays an …

Graph neural networks (GNNs) have shown remarkable success in graph-level classification
tasks. However, most of the existing GNN-based studies are based on balanced datasets …

Image data augmentation plays a crucial role in data augmentation (DA) by increasing the
quantity and diversity of labeled training data. However, existing methods have limitations …

This paper studies the problem of semi-supervised learning on graphs, which has recently
aroused widespread interest in relational data mining. The focal point of exploration in this …

Abstract Graph Representation Learning (GRL) is crucial for understanding complex graph-
structured data. This is a hot topic in Artificial Intelligence research, particularly due to its …