I am trying to figure out the right model/algorithm for a graph dataset to develop a machine learning pipeline. I have looked into Graph Neural Network(GNN) but all of the tutorials I found, trained the model on a single large graph where my dataset consists of thousands of smaller graphs like a typical machine learning dataset. Is GNN the right model for this kind of dataset? If not what should I look for?
To give an idea about the dataset, I am not still sure about the representation but the dataset will describe the AST of different code snippets.
- graphs will have different numbers of nodes and edges as they are produced from different code blocks. Also, the node types are not homogenous i.e., they have different features.
- There are multiple use cases for the model, but primarily I am interested to develop a model that predicts link or edge between two similar node.