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.

我们的研究提出了一个不确定性估计框架，可应用于任何泛化图神经网络（GNN）的基础网络上，以提高节点分类性能。该不确定性估计器模型是一个神经网络，将不确定性建模为概率分布而非离散的概率标量值。通过在 $n$ 路 $k$ 样本方式下进行训练，我们的方法在少样本学习设置中提高了分类准确率，而无需特定的元学习架构。在多个数据集和不同 GNN 基础网络上进行的实验证明了不确定性估计器对于带有 GNN 的少样本节点分类的有效性。