NN Graph

NN graphs, primarily k-nearest neighbor (k-NN) graphs, are fundamental data structures used in machine learning to represent data points as nodes connected by edges based on proximity. Current research focuses on optimizing k-NN graph construction, including adaptive methods that determine the optimal number of neighbors for each node and the development of alternative graph construction methods like those based on random projection trees or forests, which offer advantages over fixed k-NN structures. These improvements aim to enhance the performance of algorithms relying on these graphs, such as graph convolutional networks and clustering algorithms, leading to better results in applications ranging from point cloud processing to social network analysis. The ultimate goal is to create more efficient and informative graph representations that improve the accuracy and scalability of machine learning models.