  Graph neural networks (GNNs) build on the success of deep learning models by
extending them for use in graph spaces. Transfer learning has proven extremely
successful for traditional deep learning problems: resulting in faster training
and improved performance. Despite the increasing interest in GNNs and their use
cases, there is little research on their transferability. This research
demonstrates that transfer learning is effective with GNNs, and describes how
source tasks and the choice of GNN impact the ability to learn generalisable
knowledge. We perform experiments using real-world and synthetic data within
the contexts of node classification and graph classification. To this end, we
also provide a general methodology for transfer learning experimentation and
present a novel algorithm for generating synthetic graph classification tasks.
We compare the performance of GCN, GraphSAGE and GIN across both the synthetic
and real-world datasets. Our results demonstrate empirically that GNNs with
inductive operations yield statistically significantly improved transfer.
Further we show that similarity in community structure between source and
target tasks support statistically significant improvements in transfer over
and above the use of only the node attributes.
