A classic example of a node classification task is to identify bots from a given large social network of millions of users. Examples of node classification beyond social networks include classifying the function of proteins in the interactome and classifying the topic of documents based on hyperlink or citation graphs.

Examples of Node Classification Tasks

The main difference between node classification tasks and traditional supervised classification tasks is that the nodes in a graph are not independent and identically distributed, which is usually assumed in the former case. Instead, we model an interconnected set of nodes, and leverage concepts such as homophily, structural equivalence, and heterophily. Node classification tasks are thus often referred to as semi-supervised.

Categorization of Node Classification

Node classification is a machine learning task on graph data where the goal is to predict the label, which could be a type, category, or attribute, associated with all the nodes, when only given the true labels on a training set of nodes.

University of Michigan - Ann Arbor

Machine learning is inherently a problem-driven discipline, where we seek to build models that can learn from data in order to solve particular tasks. Machine learning on graphs is no different, except the usual categories of supervised and unsupervised are not necessarily the most informative or useful when it comes to graphs.

Machine Learning on Graphs

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

Graph Representation Learning by William Hamilton

Node Classification

Relation prediction (link prediction, graph completion, relation inference) is a machine learning task on graph data where the goal is to infer the missing edges of a graph, when given a set of nodes and an incomplete set of edges between these nodes.

Relation Prediction

Community detection is a machine learning task on graph data where the goal is to infer latent community structures given only the input graph.

Community Detection

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.

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