  Graphs provide a powerful means for representing complex interactions between
entities. Recently, deep learning approaches are emerging for representing and
modeling graph-structured data, although the conventional deep learning methods
(such as convolutional neural networks and recurrent neural networks) have
mainly focused on grid-structured inputs (image and audio). Leveraged by the
capability of representation learning, deep learning based techniques are
reporting promising results for graph applications by detecting structural
characteristics of graphs in an automated fashion. In this paper, we attempt to
advance deep learning for graph-structured data by incorporating another
component, transfer learning. By transferring the intrinsic geometric
information learned in the source domain, our approach can help us to construct
a model for a new but related task in the target domain without collecting new
data and without training a new model from scratch. We thoroughly test our
approach with large-scale real corpora and confirm the effectiveness of the
proposed transfer learning framework for deep learning on graphs. According to
our experiments, transfer learning is most effective when the source and target
domains bear a high level of structural similarity in their graph
representations.
