Large Input Graph

Processing large input graphs presents significant computational challenges for many machine learning tasks, prompting research into efficient algorithms and model architectures. Current efforts focus on developing scalable methods for community detection, clustering, and graph neural network (GNN) training on these large graphs, often employing techniques like subgraph sampling, low-rank approximations, and innovative indexing structures to improve performance. These advancements are crucial for handling real-world datasets in diverse fields such as social network analysis, recommendation systems, and bioinformatics, enabling more effective analysis and prediction on massive, complex networks.