  Few-shot learning presents a challenging paradigm for training discriminative
models on a few training samples representing the target classes to
discriminate. However, classification methods based on deep learning are
ill-suited for such learning as they need large amounts of training data --let
alone one-shot learning. Recently, graph neural networks (GNNs) have been
introduced to the field of network neuroscience, where the brain connectivity
is encoded in a graph. However, with scarce neuroimaging datasets particularly
for rare diseases and low-resource clinical facilities, such data-devouring
architectures might fail in learning the target task. In this paper, we take a
very different approach in training GNNs, where we aim to learn with one sample
and achieve the best performance --a formidable challenge to tackle.
Specifically, we present the first one-shot paradigm where a GNN is trained on
a single population-driven template --namely a connectional brain template
(CBT). A CBT is a compact representation of a population of brain graphs
capturing the unique connectivity patterns shared across individuals. It is
analogous to brain image atlases for neuroimaging datasets. Using a
one-representative CBT as a training sample, we alleviate the training load of
GNN models while boosting their performance across a variety of classification
and regression tasks. We demonstrate that our method significantly outperformed
benchmark one-shot learning methods with downstream classification and
time-dependent brain graph data forecasting tasks while competing with the
train-on-all conventional training strategy. Our source code can be found at
https://github.com/basiralab/one-representative-shot-learning.
