Large Scale Graph Datasets

Large-scale graph datasets present significant computational challenges for machine learning, particularly for graph neural networks (GNNs), which are used for tasks like node classification and graph prediction. Current research focuses on improving GNN scalability through techniques like subgraph training, efficient algorithms (e.g., those based on neural tangent kernels), and hardware acceleration, as well as developing methods to reduce reliance on computationally expensive message-passing. These advancements are crucial for enabling the application of GNNs to increasingly large and complex real-world problems in diverse fields such as social network analysis, drug discovery, and fraud detection.