  Many modern deep-learning techniques do not work without enormous datasets.
At the same time, several fields demand methods working in scarcity of data.
This problem is even more complex when the samples have varying structures, as
in the case of graphs. Graph representation learning techniques have recently
proven successful in a variety of domains. Nevertheless, the employed
architectures perform miserably when faced with data scarcity. On the other
hand, few-shot learning allows employing modern deep learning models in scarce
data regimes without waiving their effectiveness. In this work, we tackle the
problem of few-shot graph classification, showing that equipping a simple
distance metric learning baseline with a state-of-the-art graph embedder allows
to obtain competitive results on the task.While the simplicity of the
architecture is enough to outperform more complex ones, it also allows
straightforward additions. To this end, we show that additional improvements
may be obtained by encouraging a task-conditioned embedding space. Finally, we
propose a MixUp-based online data augmentation technique acting in the latent
space and show its effectiveness on the task.
