NeurIPS 2020

Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings

Meta Review

This paper focuses on the problem of how to develop a (scalable) graph learning technique, which has been underexplored in the domain. The proposal is a novel end-to-end graph learning framework to joint learn graph structure and graph embeddings. The philosophy behind sounds quite interesting to me, namely, sparsified graph over the fully connected graph by performing epsilon-neighborhood and adaptive graph regularization. This philosophy leads to a novel algorithm design I have never seen, i.e., Iterative Deep Graph Learning (IDGL). More importantly, IDGL can cope with both transductive and inductive settings, where the learned embeddings can be applied for many tasks. The clarity and novelty are clearly above the bar of NeurIPS. While the reviewers had some concerns on the significance, the authors did a particularly good job in their rebuttal. Thus, most of us have agreed to accept this paper for publication! Please carefully address R2' comments in the final version.