Graph Classification

Graph classification aims to automatically categorize graphs based on their structural and/or attribute properties, a crucial task with applications across diverse fields. Current research focuses on improving the efficiency and accuracy of graph neural networks (GNNs) for this purpose, exploring novel architectures like those incorporating attention mechanisms, hierarchical pooling, and disentangled representation learning, as well as developing techniques to address challenges such as oversmoothing, imbalanced datasets, and out-of-distribution generalization. These advancements are significant because they enable more effective analysis of complex relational data in domains ranging from social networks and bioinformatics to high-energy physics.