Graph classification¶
StellarGraph provides algorithms for graph classification. This folder contains demos to explain how they work and how to use them as part of a TensorFlow Keras data science workflow.
A graph classification task predicts an attribute of each graph in a collection of graphs. For instance, labelling each graph with a categorical class (binary classification or multiclass classification), or predicting a continuous number (regression). It is supervised, where the model is trained using a subset of graphs that have ground-truth labels.
Find algorithms and demos for a collection of graphs¶
This table lists all graph classification demos, including the algorithms trained and the types of graphs used.
| demo | algorithm(s) | node features | inductive |
|---|---|---|---|
| GCN Supervised Graph Classification | GCN, mean pooling | yes | yes |
| DGCNN | DeepGraphCNN | yes | yes |
See the demo index for more tasks, and a summary of each algorithm.