Structural Entropy

Structural entropy quantifies the uncertainty or randomness within the structure of data, particularly graph-structured data, and is increasingly used to improve machine learning model performance. Current research focuses on leveraging structural entropy in various applications, including sample selection for efficient learning, social bot detection, and reinforcement learning, often integrating it with graph neural networks and contrastive learning methods. This approach enhances model robustness, particularly in noisy or high-dimensional data, leading to improved accuracy, efficiency, and interpretability across diverse machine learning tasks.