We present V2V, a method for embedding each vertex in a graph as a vector in a fixed dimensional space. Inspired by methods for word embedding such as word2vec, a vertex embedding is computed through enumerating random walks in the graph, and using the resulting vertex sequences to provide the context for each vertex. This embedding allows one to use well-developed techniques from machine learning to solve graph problems such as community detection, graph visualization, and vertex label prediction. We evaluate embeddings produced by V2V through comparing results obtained using V2V with results obtained through a direct application of a graph algorithm, for community detection. Our results show that V2V provides interesting trade-offs among computation time and accuracy.