  This paper studies semi-supervised graph classification, a crucial task with
a wide range of applications in social network analysis and bioinformatics.
Recent works typically adopt graph neural networks to learn graph-level
representations for classification, failing to explicitly leverage features
derived from graph topology (e.g., paths). Moreover, when labeled data is
scarce, these methods are far from satisfactory due to their insufficient
topology exploration of unlabeled data. We address the challenge by proposing a
novel semi-supervised framework called Twin Graph Neural Network (TGNN). To
explore graph structural information from complementary views, our TGNN has a
message passing module and a graph kernel module. To fully utilize unlabeled
data, for each module, we calculate the similarity of each unlabeled graph to
other labeled graphs in the memory bank and our consistency loss encourages
consistency between two similarity distributions in different embedding spaces.
The two twin modules collaborate with each other by exchanging instance
similarity knowledge to fully explore the structure information of both labeled
and unlabeled data. We evaluate our TGNN on various public datasets and show
that it achieves strong performance.
