Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: In practice, due to the lack of data or privacy issues, the attributes of some nodes may not be accessible, which makes fair graph learning more challenging. This paper proposes a fair attribute completion method FairAC to supplement the missing information, learn the fair node embedding of the missing attribute graph, and adopt a attention mechanism to deal with the attribute missing problem to improve their performance.
The author claims that FiairAC is the first method to jointly solve the problem of graph attribute completion and graph unfairness. The experimental results show that this method has lower sacrifice and better fairness in fair learning.
strength_and_weaknesses: The author claims that FiairAC is the first method to jointly solve the problem of graph attribute completion and graph unfairness.
clarity,_quality,_novelty_and_reproducibility: This paper focuses on tackling unfairness in graph learning models with fair novelty and quality.

summary_of_the_review: My research field is not related to this manuscript and I am not very familiar with the research content.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper presents a method (FairAC) to jointly addresses the graph attribute completion and graph unfairness problems due to both feature unfairness and topological unfairness due to missing attributes.
The model uses a set of $V_{keep}$ and $V_{drop}$ nodes as in Jin et al. (2001) and uses attribute completion with attention mechanism for nodes without features (this may introduce topological unfairness which   is handled by a sensitive classifier) and uses feature unfairness mitigation for nodes with attributes by removing implicit sensitive information that is present in non-sensitive attributes via an autoencoder. Fairness is measured through Statistical Parity and Equal Opportunity. 
The experiments compare FairAC with several baselines on three datasets. It tests node classification accuracy, fairness w.r.t. sensitive attributes, and effects of sensitive attributes, missing rates, and ablations (attribute missing rate, adversarial training loss relevance, and the tradeoff hyper parameter $\beta$).
strength_and_weaknesses: With respect to the strengths, the paper is well organized, the problem is relevant to the ML community, particularly the graph-learning and GNN and network science communities. Clear goals despite some imprecisions in the definitions. Relative parsimonious model that deals with specific sub-goals to achieve the final aim of fairness in completion.
There are some areas where the paper could be improved. For instance, there are several imprecisions such as in the abstract where it is stated "FairAC can be applied to any graph and generate fair embeddings”. The evidence provided is solid but stating that it will generate fair embedding to any graph is too optimistic. The definition of matrix $\mathcal X$ is that it has a dimension of attributes $D$ yet each of the labels (and their predictions) are defined as a unidimensional $y \in{0,1}$. The problem definition is a little hand-wavy. What is a fair feature embedding? The concept of “fair attribute completion model”  relies on fair feature embedding and, although it is discussed in other paragraph that it is measured via Statistical Parity and Equal Opportunity, the concept is not formally defined. The feature unfairness description regarding how “some non-sensitive attributes could infer sensitive information” seems not to be measured by Statistical Parity (SP) and Equal Opportunity (EO). The former implies also a privacy concerned while SP and EO deal with fairness of representation. Finally, the statement "removing sensitive information implicitly contained in non-sensitive attributes" has also the implication that sensitive information removed could affect performance when the amount of datapoint from subjects belonging to a minority group is not well represented. How is that scenario handled?
clarity,_quality,_novelty_and_reproducibility: The paper is relatively clear, although there are some areas that need polishing and some concepts (most notably, the risk of inference of sensitive information) require further evaluation. The quality of the ideas are interesting and the model is relatively parsimonious. The novelty is more with respect to the application of existing tools to solve a problem in attribute prediction.
summary_of_the_review: The paper has its pros, such as the relevance, the clear stated goals and evidence that seem to support the claim and the parsimonious solution to the complex problem. However, there are several imprecisions and some statements that seem to be not directly supported by the evaluation, such as the privacy statement on inference of sensitive information. 

%%%Post Rebuttal Comment%%%
Thank you for the answer to my questions. This clarifies my doubts and raise my score.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: The paper considers the problem of fair attribute completion on graphs with missing attributes. The authors proposed the FairAC framework, which jointly completes the missing features and mitigates unfairness. FairAC leverages the attention mechanism to complete missing attributes and adopts a sensitive classifier to mitigate implicit feature unfairness as well as topological unfairness on graphs. Experimental results on three real-world datasets demonstrate the superiority of the proposed FairAC framework over baselines in terms of both node classification performance and fairness performance.
strength_and_weaknesses: Strength:
- The topic of fair attribute completion on graphs with missing attributes is relevant and natural.
- The pre-processing idea has the potential to be applied to multiple downstream tasks to improve their fairness performance.

Weaknesses:
- The novelty of the problem has not been clarified clearly. The authors mentioned that FairGNN [Dai & Wang (2021)] also involves the missing attribute problem, and claimed that the paper only assumes that a part of the sensitive attributes is missing. However, I don't see many differences between partial missing and full missing for nodes. What makes the full missing setting more challenging? What is the technical difficulty of proposing methods under this setting compared to the partial missing setting? These problems should be discussed more.
- The empirical results seem not that powerful to me. The performance of FairAC is quite close to FairGNN, sometimes better and sometimes worse with slight differences. 
clarity,_quality,_novelty_and_reproducibility: Clarity: The writing is ok, but needs more comparison with FairGNN.
Quality: The empirical results are not that powerful.
summary_of_the_review: The paper considers the problem of fair attribute completion on graphs with missing attributes and proposes the FairAC framework. The novelty of the problem has not been clarified clearly, and the empirical results seem not that powerful compared to FairGNN. 

Overall, I do not recommend acceptance.

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Thanks for the response. It addresses my questions and I raise the score.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: In this paper, the authors study an important graph embedding issue, i.e., fairness on attribute-missing graphs. They propose a new fair attribute completion method to solve it by considering both feature and topological unfairness. The experiments show comparable results of the proposed method on some benchmarks.
strength_and_weaknesses: Strengths

1.	The topic is interesting and important for the graph representation learning community.

2.	This work introduces a valuable learning problem, i.e., fairness on a graph with missing attributes. The paper is well-organized and easy to follow.

3.	The idea is new, and the proposed algorithm is rational.

Weaknesses:

1. Some works [1-4] are not addressed in the related work section nor in the experimental evaluation. In my opinion, the authors should compare their solution with these algorithms at least in the related work section.

[1] Amer: A New Attribute-Missing Network Embedding Approach. TCYB, 2022.

[2] Initializing Then Refining: A Simple Graph Attribute Imputation Network. IJCAI, 2022.

[3] Graph Convolutional Networks for Graphs Containing Missing Features. FGCS, 2021.

[4] Heterogeneous Graph Neural Network via Attribute Completion. WWW, 2021.

2. Although this paper is well-motivated, the technique contributions seem a bit weak. The used techniques such as attention mechanism, adversarial learning, and auto-encoder have been widely researched in graph domains. I wonder the reason that why combining these components contributes more to the performance.

3.  According to the results from ablation studies, the effectiveness of the proposed components seems unclear. For example, Table 2 can see no difference in the performance of some datasets.

4. Explain if the metric is for one experiment over randomized trials, e.g., randomized initialization. The results in Table 1 and Table 2 would be stronger if the metric reported were mean/std.

5. I would discuss the difference and the connection between the proposed model and existing graph fairness methods in theory.

clarity,_quality,_novelty_and_reproducibility: Clarity:Good: The paper is well organized, but the presentation has minor details that could be improved.

Quality:Good: The paper appears to be technically sound. The proofs, if applicable, appear to be correct, but I have not carefully checked the details. The experimental evaluation, if applicable, is adequate, and the results convincingly support the main claims.

Novelty:Fair: The paper contributes some new ideas or represents incremental advances.

Reproducibility: Good: key resources (e.g., proofs, code, data) are available.
summary_of_the_review: The paper has studied a very interesting problem. Besides, the idea of the paper is novel and the work is technique solid.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: The paucity of reliable data and privacy issues may render some attributes of nodes in graph data inaccessible, which is a challenge for fair graph learning. This paper develops FairAC, a fair attribute-completion method to learn a fair node embedding of a given graph. FairAC adopts an attention mechanism to deal with missing attributes; it is developed to mitigate two types of unfairness---feature unfairness from attributes and topological unfairness due to attribute completion. Experimental results show that this method may have better fairness in learning.

To evaluate the fairness of the methods compared, the authors adopt a widely-used evaluation protocol in fair graph learning and set a threshold for accuracy, since there is a trade-off between accuracy and fairness. Since the primary focus is fairness, the authors set the accuracy threshold that all methods can satisfy, e.g., around 66% on the pokec_n dataset . Tables 1 and 2 suggest that this work obtains the best fairness performance and can maintain a comparable performance on accuracy and AUC. Specifically, in comparison with the SOTA approach of FairGNN, FairAC is shown to improve performance in terms of the fairness metric \deltaSP+\deltaEO: 65%, 87%, and 67% improvement over FairGNN on the NBA, pokec_z, and pokec_n datasets respectively.

The authors are also asked to improve the presentation as suggested by the referees.

justification_for_why_not_higher_score: This work simply does not rise to the level of oral/spotlight: it is borderline. 
justification_for_why_not_lower_score: This approach to fairness seems natural and important: missing/inaccessible attributes is a very common issue.
Decision: Accept: poster