I have a dataset of graphs, several of them have different target feature dimensions, e.g.
Graph1 = Data(edge_attr=[9468], edge_index=[2, 9468], x=[299, 21], y=[299])
Graph2 = Data(edge_attr=[9622], edge_index=[2, 9622], x=[309, 21], y=[309])
As you see they contain a different amount of nodes, 299 vs 309, each target y is a continuous variable that I want to predict. The problem is since they are different sizes I would need a neural network with a dynamic output dimension, which is problematic. My solution would be to “pad” the graphs with nodes that have features equal to 0 and a target equal to 0 with no edge connections to make the graphs have the same number of nodes and targets, but I am not sure if this is a good solution. Do any of you have an idea of how to solve this problem? I am quite new to the field of geometric deep learning.
Thanks!