  Patterns stored within pre-trained deep neural networks compose large and
powerful descriptive languages that can be used for many different purposes.
Typically, deep network representations are implemented within vector embedding
spaces, which enables the use of traditional machine learning algorithms on top
of them. In this short paper we propose the construction of a graph embedding
space instead, introducing a methodology to transform the knowledge coded
within a deep convolutional network into a topological space (i.e. a network).
We outline how such graph can hold data instances, data features, relations
between instances and features, and relations among features. Finally, we
introduce some preliminary experiments to illustrate how the resultant graph
embedding space can be exploited through graph analytics algorithms.
