Before revision:  Whereas most prior work on graph neural networks uses linear functions to aggregate information from neighboring nodes, we find that non-linear aggregators such as LSTMs or transformers lead to significant improvements on zero-shot tasks.
After revision:  Our proposed TrGCN architecture computes non-linear aggregators such as LSTMs or transformers lead to significant improvements on zero-shot tasks.