I have a doubt about how I should process my dataset before training a GNN on it.
I have 300 texts and I aim to turn each one of them into a graph. They are tagger with atributes and relations from each text. No problem there.
The things is later i want to predict some relations in new graphs some missings relations.
My doubt here is which is the right (and possible) approach:
should I create a graph from every text? could I train the GNN on all of them in order to later predict on relations on new graphs?
or should I create one big graph in which every text is, lets say, a subgraph (or partiton)? In this case, if i add a new subgraph to the big graph wich contains all graphs from training, could I predict links JUST in this new subgraph?
Are these approches valid? If so, any of them is better than the other?
Thank you so much,