1. Node classification: one of the most popular benchmark tasks for graph neural networks. The standard way to apply graph neural networks to such a node classification task is to train graph neural networks in a fully-supervised manner, where we define the loss using a softmax classification function and negative log-likelihood loss.

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For most current applications, graph neural networks are used in one of three tasks: node classification, graph classification, or relation prediction.

Applications and Loss Functions

Graph Neural Networks Node classification

2. Graph classification: Similar to node classification, applications on graph-level classification are popular as benchmark tasks. Historically, kernel methods were popular for graph classification, and—as a result—some of the most popular early benchmarks for graph classification were adapted from the kernel literature, such as tasks involving the classification of enzyme properties based on graph-based representations.

Graph neural networks graph classification

3. Relation prediction: graph neural networks are used in relation prediction tasks, such as recommender systems and knowledge graph completion.
Pre-training graph neural networks with a neighborhood reconstruction losses could be a useful strategy to improve performance on a downstream classification task.

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