  Multitude of deep learning models have been proposed for node classification
in graphs. However, they tend to perform poorly under labeled-data scarcity.
Although Few-shot learning for graphs has been introduced to overcome this
problem, the existing models are not easily adaptable for generic graph
learning frameworks like Graph Neural Networks (GNNs). Our work proposes an
Uncertainty Estimator framework that can be applied on top of any generic GNN
backbone network (which are typically designed for supervised/semi-supervised
node classification) to improve the node classification performance. A neural
network is used to model the Uncertainty Estimator as a probability
distribution rather than probabilistic discrete scalar values. We train these
models under the classic episodic learning paradigm in the $n$-way, $k$-shot
fashion, in an end-to-end setting. Our work demonstrates that implementation of
the uncertainty estimator on a GNN backbone network improves the classification
accuracy under Few-shot setting without any meta-learning specific
architecture. We conduct experiments on multiple datasets under different
Few-shot settings and different GNN-based backbone networks. Our method
outperforms the baselines, which demonstrates the efficacy of the Uncertainty
Estimator for Few-shot node classification on graphs with a GNN.
