Affinity Score

Affinity scores quantify the similarity or relationship between different entities, such as data points, tasks, or nodes in a network, serving as crucial inputs for various machine learning algorithms. Current research focuses on developing novel affinity metrics tailored to specific data types (e.g., discrete distributions, graphs) and incorporating them into advanced models like personalized federated learning and graph neural networks to improve performance and address challenges like class imbalance and negative transfer in multi-task learning. These advancements have significant implications for improving the accuracy and efficiency of machine learning across diverse applications, including bias detection in large language models and community detection in network analysis.