Weakness:
1.I think the work is lack of novelty as the work GPN[1] has already proposed to add node importance score in the calculation of class prototype and the paper only give a theoretical analysis on it.
2.The experiment part is not sufficient enough. (1) For few-shot graph node classification problem to predict nodes with novel labels, there are some methods that the paper does not compare with. For example, G-Meta is mentioned in the related works but not compared in the experiments. A recent work TENT[2] is not mentioned in related works. As far as I know, the above two approaches can be applied in the problem setting in the paper. (2) For the approach proposed in the paper, there is no detailed ablation study for the functionalities of each part designed. (3) It is better to add a case study part to show the strength of the proposed method by an example. Concerns:
1.The paper consider the node importance among nodes with same label in support set. In 1-shot scenario, how node importance can be used? I also find that the experiment part in the paper does not include the 1-shot paper setting, but related works such as RALE have 1-shot setting, why?
2.The paper says that the theory of node importance can be applied to other domains. I think there should be an example to verify that conclusion.
3.In section 5.3, ‘we get access to abundant nodes belonging to each class’. I do not think this is always true as there might be a class in the training set that only has few samples given the long-tailed distribution of samples in most graph datasets.
[1] Ding et al. Graph Prototypical Networks for Few-shot Learning on Attributed Networks
[2] Wang et al. Task-Adaptive Few-shot Node Classification
1.The paper consider the node importance among nodes with same label in support set. In 1-shot scenario, how node importance can be used? I also find that the experiment part in the paper does not include the 1-shot paper setting, but related works such as RALE have 1-shot setting, why?