Is it possible to deal with datasets of graphs with different number of nodes in graph nural networks? I'm dealing with a graph classification problem. In my dataset, each graph has som specific number of nodes. The number of nodes has a range of 1-1000 nodes. At inference time (after training), the model could be used for classifying graphs with an even bigger number of nodes in inference time.
Is it possible to do this with graph neural networks? Should I consider some maximum number of nodes or something like this?
Could you provide references or code examples for doing this?


Is it possible to do this with graph neural networks?

Yes, this is possible using various GNNs architectures, and you usually do not need to set a maximum number or nodes.
For example, Tox21 dataset has chemical compounds which has different number of nodes, and the aim is to predict if a compound is toxic or not. You can find some papers here, or search google scholar to find GNN papers that benchmark on it.
