DeepWalk is a machine learning algorithm designed for learning representations of vertices in a graph. It combines random walks and Word2Vec, a popular natural language processing technique, to generate embeddings that capture the structure and relationships within the graph. By simulating random walks, it creates sequences of nodes that can be treated like sentences, allowing the model to learn meaningful patterns. The main advantage of DeepWalk is its ability to handle large-scale graphs efficiently while preserving the local and global structure of the data. This makes it useful for various applications, including social network analysis, recommendation systems, and biological network studies.