Graph2Vec implements a neural embedding framework that learns task-agnostic distributed representations of arbitrarily sized graphs. It uses a subsampling and rooted subgraph approach inspired by document embedding techniques to capture graph structure. The learned embeddings can be used for downstream tasks including graph classification, clustering, and as features for supervised learning pipelines.