Local Graph

Local graph research focuses on analyzing and leveraging the structure and information contained within localized portions of larger graphs, aiming to improve efficiency and address challenges in distributed data settings. Current research emphasizes the development of graph neural networks (GNNs) and contrastive learning methods tailored to local graph structures, often incorporating techniques like attention mechanisms and subgraph federated learning to handle incomplete or distributed data. This work is significant for its potential to enhance the scalability and privacy of graph-based machine learning applications across diverse domains, including visual place recognition, drug discovery, and social network analysis.