In Graph Data Science, labeling involves assigning meaningful attributes to nodes and edges in a graph, providing context to entities and relationships. Node labels describe individual characteristics, while edge labels convey relationship properties. Effective labeling is essential for tasks like graph algorithms and anomaly detection, enhancing interpretability and insight extraction from complex interconnected data.

Graph labeling is a versatile process that adapts to the specific characteristics and goals of the graph data science task at hand. It helps transform raw graph data into a format suitable for various analytical and predictive tasks.