  Invertible transformation of large graphs into fixed dimensional vectors
(embeddings) remains a challenge. Its overcoming would reduce any operation on
graphs to an operation in a vector space. However, most existing methods are
limited to graphs with tens of vertices. In this paper we address the above
challenge with recursive neural networks - the encoder and the decoder. The
encoder network transforms embeddings of subgraphs into embeddings of larger
subgraphs, and eventually into the embedding of the input graph. The decoder
does the opposite. The dimension of the embeddings is constant regardless of
the size of the (sub)graphs. Simulation experiments presented in this paper
confirm that our proposed graph autoencoder, ReGAE, can handle graphs with even
thousands of vertices.
