Diffusion Distance

Diffusion distance is a measure of similarity between data points based on the connectivity and density of the underlying data manifold, often visualized as the time it takes a random walk to travel between points. Current research focuses on leveraging diffusion distance within various machine learning frameworks, including diffusion models and graph neural networks, to improve tasks such as 3D point cloud processing, counterfactual explanation generation, and hierarchical representation learning. This approach offers advantages in handling noisy data, generating more realistic and consistent outputs, and providing more interpretable model explanations, impacting fields ranging from computer vision and drug discovery to data analysis and AI explainability.