Large Scale Graph

Large-scale graph analysis focuses on efficiently processing and extracting insights from massive graph datasets, aiming to overcome computational limitations and privacy concerns inherent in traditional methods. Current research emphasizes developing scalable graph neural networks (GNNs) and graph transformers, along with techniques like graph condensation and federated learning, to handle the size and distributed nature of these graphs. These advancements are crucial for improving the performance of various machine learning tasks on large graphs, including node classification, graph partitioning, and recommendation systems, while also addressing challenges related to data privacy and security. The resulting improvements in efficiency and scalability have significant implications for diverse fields, ranging from social network analysis to drug discovery.