  We propose to study the problem of few shot graph classification in graph
neural networks (GNNs) to recognize unseen classes, given limited labeled graph
examples. Despite several interesting GNN variants being proposed recently for
node and graph classification tasks, when faced with scarce labeled examples in
the few shot setting, these GNNs exhibit significant loss in classification
performance. Here, we present an approach where a probability measure is
assigned to each graph based on the spectrum of the graphs normalized
Laplacian. This enables us to accordingly cluster the graph base labels
associated with each graph into super classes, where the Lp Wasserstein
distance serves as our underlying distance metric. Subsequently, a super graph
constructed based on the super classes is then fed to our proposed GNN
framework which exploits the latent inter class relationships made explicit by
the super graph to achieve better class label separation among the graphs. We
conduct exhaustive empirical evaluations of our proposed method and show that
it outperforms both the adaptation of state of the art graph classification
methods to few shot scenario and our naive baseline GNNs. Additionally, we also
extend and study the behavior of our method to semi supervised and active
learning scenarios.
