Graph Level Classification

Graph-level classification aims to predict the properties of entire graphs, rather than individual nodes, using machine learning. Current research focuses on improving the expressiveness and efficiency of graph neural networks (GNNs), exploring alternative architectures beyond convolutional approaches, and developing data preprocessing techniques like graph tokenization to enhance model performance. These advancements are crucial for various applications, including drug discovery, social network analysis, and protein structure prediction, where understanding the properties of entire networks is essential. Furthermore, significant effort is dedicated to improving the robustness and explainability of GNNs for graph-level classification, addressing issues like long-tailed data distributions and adversarial attacks.