Node embedding is a technique used in machine learning to represent nodes in a graph as vectors in a continuous vector space. This allows for easier analysis and processing of graph data, as it transforms complex relationships into numerical formats that algorithms can understand. By capturing the structural information and relationships between nodes, node embedding helps improve tasks like node classification, link prediction, and graph clustering. Popular methods for generating node embeddings include DeepWalk and Node2Vec, which leverage random walks and neighborhood sampling to create meaningful representations of nodes based on their connectivity.