Large Graph

Large graph analysis focuses on developing efficient algorithms and models to process and analyze graphs with billions of nodes and edges, exceeding the capacity of traditional methods. Current research emphasizes scalable graph neural networks (GNNs), often employing sampling-based training and novel architectures like graph transformers to overcome computational limitations, alongside techniques like graph condensation and distributed training to handle massive datasets. These advancements are crucial for tackling real-world problems in diverse fields, including social network analysis, recommendation systems, and drug discovery, where large graphs are ubiquitous and require efficient processing for meaningful insights.