Weakness: 1. The motivation of this work should be further justified. In few-shot learning, we usually consider how to leverage a few instances to learn a generalizable model. This paper defines and creates a few-shot situation for graph link prediction, but the proposed method does not consider how to effectively use “few-shot” and how to guarantee the trained model can be generalized well to new tasks with 0/few training steps. 2. The definition of “domain” in this paper is unclear. For instance, why select multiple domains from the same single graph in ogbn-products? Should we consider the selected domains as “different domains”? 3. The application of adversarial learning in few-shot learning is confusing. Adversarial learning in domain adaptation aims to learn domain-invariant representations, but why do we need such kind of representation in few-shot learning?
1. The motivation of this work should be further justified. In few-shot learning, we usually consider how to leverage a few instances to learn a generalizable model. This paper defines and creates a few-shot situation for graph link prediction, but the proposed method does not consider how to effectively use “few-shot” and how to guarantee the trained model can be generalized well to new tasks with 0/few training steps.