Graph Sample

Graph sample selection and generation are crucial for efficient and effective machine learning on graph-structured data, aiming to identify or create representative subsets of nodes and edges that capture essential structural information. Current research focuses on developing algorithms that incorporate both local (e.g., node importance) and global (e.g., graph entropy) properties to improve sample quality and diversity, often employing techniques like contrastive learning and Markov Chain Monte Carlo methods. These advancements enable scalable graph neural network training and improved performance on various tasks, including node classification and graph generation, impacting fields like computer vision and intelligent education.