This document summarizes graph-based semi-supervised learning techniques. It discusses how these techniques can effectively propagate labels from a small set of labeled data to large amounts of unlabeled data. Graph-based techniques are well-suited for real-world data that exists in graph form and can represent multi-modal, multi-format data sources. The document outlines key concepts like representing instances as graph vertices and connections as edges. It also describes popular graph-based SSL algorithms and challenges like dealing with large-scale data and noisy labels. Finally, it proposes a novel technique using hypergraph heat diffusion and summarizes the components and pipeline of this approach.