  We propose a novel subgraph image representation for classification of
network fragments with the targets being their parent networks. The graph image
representation is based on 2D image embeddings of adjacency matrices. We use
this image representation in two modes. First, as the input to a machine
learning algorithm. Second, as the input to a pure transfer learner. Our
conclusions from several datasets are that (a) deep learning using our
structured image features performs the best compared to benchmark graph kernel
and classical features based methods; and, (b) pure transfer learning works
effectively with minimum interference from the user and is robust against small
data.
