  Signed Graph Neural Networks (SGNNs) have recently gained attention as an
effective tool for several learning tasks on signed networks, i.e., graphs
where edges have an associated polarity. One of these tasks is to predict the
polarity of the links for which this information is missing, starting from the
network structure and the other available polarities. However, when the
available polarities are few and potentially noisy, such a task becomes
challenging.
  In this work, we devise a semi-supervised learning framework that builds
around the novel concept of \emph{multiscale social balance} to improve the
prediction of link polarities in settings characterized by limited data
quantity and quality. Our model-agnostic approach can seamlessly integrate with
any SGNN architecture, dynamically reweighting the importance of each data
sample while making strategic use of the structural information from unlabeled
edges combined with social balance theory.
  Empirical validation demonstrates that our approach outperforms established
baseline models, effectively addressing the limitations imposed by noisy and
sparse data. This result underlines the benefits of incorporating multiscale
social balance into SGNNs, opening new avenues for robust and accurate
predictions in signed network analysis.
