The supervised graph embedding roadmap
In supervised learning, a training set consists of a sequence of ordered pairs (x, y), where x is a set of input features (often signals defined on graphs) and y is the output label assigned to it. The goal of ML models, then, is to learn the function mapping each x value to each y value. Here, y can be either a categorical or a continuous variable, depending on whether we are addressing a classification or a regression problem. Common supervised tasks include predicting user properties in a large social network or predicting molecules’ attributes, where each molecule is a graph. Sometimes, however, not all instances can be provided with a label. In this scenario, a typical dataset consists of a small set of labeled instances and a larger set of unlabeled instances. For such situations, semi-supervised learning is proposed, whereby algorithms aim to exploit label dependency information reflected by available label information in order...
