I build a heterogeneous Graph using Pytorch. The graph structure looks like this: (author, writes, paper), (paper, authored, author), (paper, cites, paper) and I try to predict the “authored” relation based on the cited papers and their references.
Each paper and author represent unique classes with unique labels. Therefore I need to propagate that information somehow. As the current solution, I converted the ids through an embedding layer and inserted them into the nodes’ features. Are there current models out there that can learn from their neighboring labels directly?
And is it the only and proper way to convert it to a lower space vector using embeddings?
(One-hot encoding did not work due to low memory.)
Thanks in advance.