Has there been any research on combining a graph database such as pregel and deep learning? For example, take a graph of encoders

where

encoder+decoder = autoencoder (contractive/denoising/sparse) or encoder = ica or encoder = sparse pca or encoder = non-negative matrix factorization

The encoder graph is directed and acyclic

The output of two or more encoders must match the input of the one their plug into. In the simplest case, input = 2*hidden, then 2 encoders plug into another encoder.

Pregel is a graph database (there are open source versions too) which can handle trillion+ connections. As far as I can tell, the database is almost perfectly suited to the encoder graph formulation of unsupervised deep learning, and should probably take only a few lines of code.

Is there any obvious flaws in the above, has it been already tried?