Graph embedding is a technique used to represent the nodes and edges of a graph in a continuous vector space. This allows complex graph structures to be transformed into numerical formats that can be easily processed by machine learning algorithms. By capturing the relationships and properties of the graph, embeddings facilitate tasks like node classification, link prediction, and clustering. The process typically involves algorithms that learn to map each node to a vector while preserving the graph's topology. Popular methods include DeepWalk, Node2Vec, and Graph Neural Networks, which leverage random walks or neural architectures to generate meaningful embeddings for various applications.