Hello DGL!
I’m asking another quick question coming out of personal curiosity. Suppose I’m conducting a link prediction under the following two scenarios:
case 1. node features - an n-dim zero vector for all nodes, edge features - none
case 2. node features - an n-dim zero vector for all nodes, edge features - a single feature(i.e., a column vector) assumed to help prediction.
Essentially, case 2 should outperform case 1. But my question is how case 1 can make some performance without any information but graph structure? Is it just because some learnable weights inside the model are adjusted through backpropagation in training?
Thanks!