The supervised graph embedding roadmap
In SL, 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 the ML models, then, is to learn the function mapping each x value to each y value. 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-SL (SSL) is proposed, whereby algorithms aim to exploit label dependency information reflected by available label information in order to learn the predicting function for the unlabeled samples.
With regard to supervised graph ML techniques, many algorithms have been developed. However as previously...
