Graph Datasets

Graph datasets are collections of data represented as networks of interconnected nodes and edges, used to model diverse relationships in various domains. Current research focuses on developing robust and efficient graph neural network (GNN) architectures for tasks like node classification, link prediction, and graph generation, addressing challenges such as data scarcity, imbalanced classes, and distribution shifts across datasets. These advancements are crucial for improving the interpretability and fairness of GNNs, as well as enabling scalable federated learning approaches for privacy-sensitive applications. The development of comprehensive benchmarks and standardized evaluation protocols is also a key area of focus, facilitating more reliable comparisons and driving progress in the field.