Examples of relation prediction tasks include: recommending content to users in social platforms, predicting drug side-effects, or inferring new facts in a relational databases.

University of Michigan - Ann Arbor

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

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

Examples of Relation Prediction Tasks

Like node classification, relation prediction blurs the boundaries of traditional machine learning categories — often being referred to as both supervised and unsupervised — and it requires inductive biases that are specific to the graph domain. In addition, like node classification, there are many variants of relation prediction, including settings where the predictions are made over a single, fixed graph, as well as settings where relations must be predicted across multiple disjoint graphs.

Categorization of Relation Prediction

Graph neural networks (GNNs) are commonly applied to relation prediction tasks, such as recommender systems and knowledge graph completion. In these contexts, pre-training GNNs with a neighborhood reconstruction loss can be a useful strategy to improve performance on downstream classification tasks.

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