Examples of graph classification, regression, and clustering include: building a regression model that could predict that molecule’s toxicity or solubility and building a classification model to detect whether a computer program is malicious by analyzing a graph-based representation of its syntax and data flow.

University of Michigan - Ann Arbor

In this set of machine learning tasks on graph data, instead of making predictions over the individual components of a single graph, we are given a dataset of multiple different graphs. In graph classification and regression, the goal is to make independent predictions specific to each graph. On the other hand, in graph clustering, the goal is to learn an unsupervised measure of similarity between pairs of graphs.

Graph Classification, Regression, and Clustering

Hamilton, W.L. Graph Representation Learning. *Synthesis Lectures on Artificial Intelligence and Machine Learning*. 2020, 14(3), 1–159. https://doi.org/10.2200/S01045ED1V01Y202009AIM046

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