No features + sparse labels training

[Alekos92]

Hello and thanks for your work.

I would like to apply the GCN architecture on a graph whose nodes have no features, and also very few nodes have labels. More specifically, this is going to be a graph of words, where related words are connected with an edge, and also I have some document nodes that are connected to the words they contain. Some document nodes have labels, and the rest are left to be predicted. Word nodes are just there to help associate document nodes with one another, and, hopefully, propagate the labels from the training document nodes to the testing document nodes. The first dataset I tried is OHSUMED, if that makes a difference.

I started transforming the code to fit my needs, but I have a couple of issues:

1. What do I replace the feature matrix with? F is a no. of nodes x features size matrix, that I have no way to populate. What I tried was to set it as an identity matrix, but that seems random. Also, I tried to set this node to features matrix as another trainable parameter.

2. In the original problem, every node has a label associated with it. However, in my case, less than .1% of the nodes have a label. I decided to just provide the indexes of the adjacency matrix that are associated with the training/validating/testing document nodes. Is there an optimal way to represent non-labeled nodes?

So far, I haven't been able to get the model to work in this problem. With several permutations of the modifications above, I get an accuracy of about 20%, far below my other baselines. Am I missing something obvious in the model definition or the optimization process?

Any help is welcome.