Summary
The authors propose applying the MAML methodology to hyperbolic GNNs in a novel local manner, along with continuous label prototypes, that enables them to scale the HNN approach from a few thousand nodes to a few million nodes.
The authors provide theoretical justification for this local H-GRAM approach via theorems 1 and 2 and demonstrate its efficacy via a large set of experimental comparisons in Sec. 4 and 5.
Strengths
1. The four research questions from Sec. 4 regarding the novel HGRAM approach are comprehensively answered via comparison on multiple different datasets with competing Euclidean MAML approaches, standard hyperbolic baselines, other graph MAML approaches such as G-Meta [17], Meta-GNN [38], protoNET[29] and ablation studies. H-GRAM clearly outperforms competing meta-learning / protoNET approaches on large graphs in Table 1 and is comparable with competing hyperbolic approaches on small graphs in Table 2.
2. The authors provide theoretical justification for their local H-GRAM approach via theorems 1 and 2.
Weaknesses
1.
Some of the mathematical presentation could be shortened, e.g., the same symbol $D_{g\mu}^{p_i}$ is defined in both theorems 1 and 2.
2.
The authors limit their meta-learning enhancements to hyperbolic neural networks, but it is not clear if their enhancements can be applied to superior pseudo-Riemannian approaches, e.g., Pseudo-Riemannian Graph Convolutional Networks, NeurIPS 2022, and Ultrahyperbolic Neural Networks, NeurIPS 2021.
3.
The authors' approach seems quite similar to G-Meta [17], although their experimental results seem to be slightly better in Table 1. Presumably it is the hyperbolic modeling (and RSGD) or the continuous label prototypes that allows H-GRAM to outperform G-Meta, but such explanations or other explanations are not discussed.
4.
In Sec. 5.4, it is not discussed why HGCN outperforms HMLP and HAT. In other studies, attention-based GNNs sometimes outperform GCNs, so some explanation appears to be necessary. The SG, SL setting on the Cora dataset from Table 2 could be considered in the ablation study in Table 3 to help answer this question.
Questions
Please note the questions inherent in weakness 2, 3 and 4.
Rating
8: Strong Accept: Technically strong paper, with novel ideas, excellent impact on at least one area, or high-to-excellent impact on multiple areas, with excellent evaluation, resources, and reproducibility, and no unaddressed ethical considerations.
Confidence
4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Limitations
It is not clear if weakness 2 above constitutes a limitation or whether it is simply a case where no theoretical justification can be provided for an ulterhyperbolic-GRAM variant, for example.