  In the domain of semi-supervised learning, the current approaches
insufficiently exploit the potential of considering inter-instance
relationships among (un)labeled data. In this work, we address this limitation
by providing an approach for inferring latent graphs that capture the intrinsic
data relationships. By leveraging graph-based representations, our approach
facilitates the seamless propagation of information throughout the graph,
effectively incorporating global and local knowledge. Through evaluations on
biomedical tabular datasets, we compare the capabilities of our approach to
other contemporary methods. Our work demonstrates the significance of
inter-instance relationship discovery as practical means for constructing
robust latent graphs to enhance semi-supervised learning techniques. The
experiments show that the proposed methodology outperforms contemporary
state-of-the-art methods for (semi-)supervised learning on three biomedical
datasets.
