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We propose a novel subgraph image representation for classification ofnetwork fragments with the targets being their parent networks. The graph imagerepresentation is based on 2D image embeddings of adjacency matrices. We usethis image representation in two modes. First, as the input to a machinelearning algorithm. Second, as the input to a pure transfer learner. Ourconclusions from several datasets are that (a) deep learning using ourstructured image features performs the best compared to benchmark graph kerneland classical features based methods; and, (b) pure transfer learning workseffectively with minimum interference from the user and is robust against smalldata.

Network Signatures from Image Representation of Adjacency Matrices: Deep/Transfer Learning for Subgraph Classification