DeepWalk is an unsupervised learning algorithm that learns latent representations of vertices in a graph by simulating random walks and treating these walks as sentences in a language model. It leverages the Skip-Gram model to maximize the co-occurrence probability among vertices in short random walks, effectively capturing the neighborhood structure of nodes in the graph.