Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: The authors tackle the problem of imputation in graphs, in which we want to predict missing node features in graph-structured data. Their approach uses a strategy involving pseudo-confidence (PC), where the PC of a given feature is proportional to alpha^S, where S is the corresponding node's nearest labeled neighbor. They combine this with a propagation strategy which shares information between features of the same node and between neighboring nodes. 

The authors evaluate their method on a benchmark data set OGN. They follow (Rossi et al, 2021) in their evaluation. Their primary evaluation is on a data set from arXiv, in which nodes represent papers, edges denote citations between papers and node features are a representation of the words used in the paper's title and abstract. They hold out a subset of features of each node and impute these held-out features. They show that on these tasks their method outperforms existing methods including GCNMF and Feature Propagation. 
strength_and_weaknesses: Overall, the paper is understandable, the method is reasonable and the experimental results are good. As described below, I am not sure about the methodological novelty. The application impact seems high, although this is also hard to tell because the benchmarks used are on relatively contrived tasks. 

My main concern is that I could not tell what parts of the proposed method are novel. This is partly because I am not too familiar with graph imputation. The proposed method contains many components and all components are described as though they are novel. In particular, the authors indicate that their main contribution as "pseudoconfidence" as alpha^S, where S is the distance in the graph.  They should explain that this is a common strategy used in random walk methods, Monte Carlo reinforcement learning, and many other methods. Similarly, they should explain and cite the inspiration for each other part of their method. It seems likely that there are novel components and novel ways in which preexisting components are combined, but I could not work out what these are. 

What real-world problems have 99% missing features?
clarity,_quality,_novelty_and_reproducibility: See above.
summary_of_the_review: See above.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: This paper proposes an imputation method for missing node features in graph neural networks. There is a node-wise diffusion as well as channel-wise diffusion that is based on the "pseudo confidence" of a missing feature node, which is inversely proportional to the distance to the closest observed feature node. Semi-supervied learning and link prediction experiments are conducted with good results in the very low sampling regime (0.5%).
strength_and_weaknesses: Strength: The idea is simple and easy to understand. Performance on the 0.5% sampling setting is impressive. Notations are explicitly defined and derivations are explained well. There are enough implementation details to reproduce the experiments. Extensive hyperparameter search was done (reported in appendix).

Weakness: The advantage compared to feature propagation (FP) is not yet convincing. 
Since some of the concepts are straightforward, some sections (e.g. section 3.4) could be simplified to make room for more experiments that go beyond the benchmark datasets. For example, an experiment with simulated data to understand the model's behavior better could have made the paper's argument stronger.
clarity,_quality,_novelty_and_reproducibility: The paper is well-written, and provides enough detail to reproduce the experiments.
summary_of_the_review: I think the idea presented in this paper is interesting, and it is explained well. There are also extensive experiments with promising results. But it's not clear why this ~1% gain in accuracy makes this better than the very simple FP.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: The paper targets problems with graph learning tasks containing missing node features. 
Their key idea is to assign different pseudo-confidence to each imputed channel features. 
The proposed imputation scheme includes two processes.
The first one is the feature imputation using channel-wise inter-node diffusion, and the second is the feature refinement via node-wise inter-channel propagation.
strength_and_weaknesses: Strength :

The problem is clearly described and the methods are well presented.
The scheme can even handle at an exceedingly high missing rate (e.g., 99.5%) and achieves state-of-the-art accuracy for both semi-supervised node classification and link prediction on various datasets containing a high rate of missing features.

Weaknesses:
I did not find notable weaknesses of this paper.
clarity,_quality,_novelty_and_reproducibility: Overall, the paper is clearly written. 
The quality of the methods looks sound, and the novelty is fair.
summary_of_the_review: The work motivates by an interesting and practical problem when the graph learning is conducted in presence of missing node features.
Overall, the methods look sound and reasonable.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: The paper considers the problem of node feature imputation in graphs and introduces a confidence measure that is assigned to each imputed feature. The robustness of the approach is demonstrated on semi-supervised node classification and link prediction, where it is shown to endure high rate of missing features. 

The reviewers and AC all agree that the approach presented in this paper is intuitive, interesting and promising, and that the exposition is very clear.  The empirical evaluation is extensive and shed light on many interesting aspects of the approach. The author feedback clarifying novelty is very much appreciated. In addition, the new experiment convincingly demonstrate the superiority of the proposed approach. 

As future work, it would be interesting to study the "active learning" setting where the confidence measure could be used to prioritize which missing features to acquire. See e.g.  Desjardins, Marie, James MacGlashan, and Kiri L. Wagstaff. "Confidence-based feature acquisition to minimize training and test costs." Proceedings of the 2010 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2010.
justification_for_why_not_higher_score: While the paper makes some exciting contributions, these might not be ground breaking enough for a spotlight. However, I am open to raising my score if recalibration is needed.
justification_for_why_not_lower_score: The paper makes novel contributions on an important topic, the presentation is clear and the authors have satisfactorily address all the concerns raised by the reviewers.
Decision: Accept: poster