DeepWalk learns low-dimensional representations for vertices in graphs by treating short random walks as sentences and applying the skip-gram algorithm (analogous to Word2Vec). The learned embeddings capture graph structure and can be used as features for downstream tasks like multi-label node classification. Input formats include adjacency lists, edge lists, and MATLAB sparse matrices.