Graph Classification Datasets

Graph classification datasets are crucial for evaluating and developing graph neural networks (GNNs), which are used to analyze relational data. Current research focuses on improving dataset effectiveness by addressing issues like the surprisingly strong performance of simple models on existing benchmarks, developing more efficient graph distillation techniques to create smaller, representative datasets, and enhancing the interpretability of GNN predictions through methods like subgraph sparsification. These efforts aim to create more robust and informative benchmarks, ultimately leading to better GNN models and a deeper understanding of their capabilities for various applications, including anomaly detection and domain adaptation.