Hi! I’m new to PyTorch geometric, but my understanding is that all available examples are usually around node/graph classification while I’d like to do a signal classification.
So, the feature matrix X will have (n,m) dimensions, y will be (1,n) and edges then (2,m).
An example could be a feature matrix where for every author we have information about being involved in a certain paper (then n authors and m papers). Then we know which paper cited which other paper (adjacency matrix m*m or edge list 2 by m) and we want to predict the department for each author. This way we have a graph of features that is exactly the same for every author.
So, I could reformulate this as a graph classification problem and just represent each author as a graph, but this does not look very efficient, especially if there are a lot of papers.
Could someone point me towards a code example with a similar approach? Or tell me what can be modified in usual examples to make it work for my problem?