I have a network of labelled digraphs and a I need to perform a unsupervised learning algorithm on this data.
I am interested in embedding a network of description logic documents in a vector space using the ideas in the paper Co-embedding Attributed Networks (which is a variational auto-encoder). look at the picture:
The first step is to represent each digraph as a feature vector in a vector space.
I have googled a lot but I haven't found good explanations (I also search for supervised algorithms like support vector machine working on a set of labelled digraphs but google doesn't retrieve good results).
My question is: How can I represent a set of labelled digraphs into a vector for learning purposes? (this representation algorithm must have linear complexity)