Node embeddings are a way to represent nodes in a graph as vectors in a continuous vector space. This technique helps capture the relationships and features of nodes, making it easier to analyze and process graph data. By converting nodes into numerical representations, machine learning algorithms can better understand the structure and properties of the graph. These embeddings are often generated using methods like DeepWalk or Node2Vec, which leverage random walks or neighborhood sampling to learn the embeddings. The resulting vectors can be used for various tasks, such as node classification, link prediction, and graph clustering, enhancing the performance of models dealing with graph-structured data.