Neighborhood Graph

Neighborhood graphs represent data points as nodes, connected by edges reflecting proximity or similarity, enabling efficient analysis of complex relationships. Current research focuses on improving the construction and application of these graphs, including developing adaptive methods that handle varying data densities and noise, and employing graph neural networks for tasks like subgraph counting and out-of-distribution detection. These advancements enhance the scalability and accuracy of neighborhood graph-based algorithms, impacting diverse fields such as machine learning, dimensionality reduction, and similarity search by providing more robust and efficient tools for data analysis.