Learning to quantify graph nodes

Quantification (Esuli et al. 2023; González et al. 2017) is the machine learning task of estimating the prevalence (or proportions) of each class in a dataset. Unlike standard classification, which focuses on predicting a label for each individual example, quantification works at the aggregate level by estimating the overall fraction of unlabeled instances belonging to each class. Real-world applications of quantification include but are not limited to ecological modeling (González et al. 2017) (i.e., to characterize entire populations of living species) and market research (Sebastiani 2018) (i.e., for estimating market shares of different products or services). Quantification methods are explicitly designed to account for dataset shift, which occurs when the statistical properties of the training data differ from those of the test data, due to changes in input features, labels, or their relationships. Most quantification methods are tailored to one specific type of dataset shift, namely, prior probability shift (PPS), also referred to as "label shift" (Storkey 2009).