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

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.

Relation Prediction with Graph Neural Networks

Node classification is one of the most common benchmark tasks for graph neural networks (GNNs). The standard approach trains a GNN in a fully-supervised manner: node embeddings are passed through a classifier (e.g., an MLP followed by a softmax) and the model is optimized by minimizing the negative log-likelihood (cross-entropy) loss between predicted and true node labels.

Fully-Supervised Node Classification with Graph Neural Networks

Graph neural network prediction tasks are commonly classified by their prediction target: node classification predicts labels for individual nodes, graph classification predicts labels for entire graphs, and relation prediction infers missing or likely edges between nodes.

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Graph neural networks capture the internal graph structures of natural language, like syntactic and semantic parse trees.

Graph Neural Networks

It is important to research on how we can make graph neural networks more powerful than WL algorithm because there are some research that indicates that models such as message-passing graph neural networks are no more powerful than the WL algorithm.

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