Title: A Unified Framework for Fair Graph Generation: Theoretical Guarantees and Empirical Advances
Venue: NeurIPS 2025
Accepted: yes
Keywords: Fairness

Graph Generation

GNN
Abstract: Graph generation models play pivotal roles in many real-world applications, from data augmentation to privacy-preserving. Despite their deployment successes, existing approaches often exhibit fairness issues, limiting their adoption in high-risk decision-making applications. Most existing fair graph generation works are based on autoregressive models that suffer from ordering sensitivity, while primarily addressing structural bias and overlooking the critical issue of feature bias. To this end, we propose FairGEM, a novel one-shot graph generation framework designed to mitigate both graph structural bias and node feature bias simultaneously. Furthermore, our theoretical analysis establishes that FairGEM delivers substantially stronger fairness guarantees than existing models while preserving generation quality. Extensive experiments across multiple real-world datasets demonstrate that FairGEM achieves superior performance in both generation quality and fairness.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a new method for fair graph generation. Starting with an existing graph, the goal is to produce a statistically similar one. Existing methods often emphasize irrelevant disparities between nodes, a phenomenon this approach aims to avoid.\n\nThe authors focus on a graph diffusion process to achieve fairness in both the graph's structure and its node features. The core idea is to apply penalties to two separate diffusion processes: one for the adjacency matrix (A) and another for the node features (F).\n\nThe structural fairness regularizer quantifies the discrepancy in reconstruction errors between intra-group and inter-group edges. For fair diffusion of node features, the regularization occurs in two steps. First, a Variational Autoencoder (VAE) disentangles legitimate group disparities from irrelevant ones. This partition is then used to construct a Maximum Mean Discrepancy (MMD) regularizer.\n\nSeveral experiments were conducted to assess the accuracy and fairness of the generated graph. The proposed method was compared to six other methods across four datasets. It systematically offered the best fairness performance while maintaining an accuracy similar to the other models. Experiments on a downstream task also demonstrated good fairness and accuracy. Finally, further experiments confirmed that all three steps of the regularization method are necessary to achieve these strong results.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\nThe paper offers strong theoretical guarantees and presents compelling experiments that validate the soundness and relevance of the proposed method. The subject of fair graph generation, especially in a setup that addresses fairness in both graph structure and node features, appears to be a relatively new and unexplored area.\n\n**Weaknesses**\n\nCertain sections of the paper could benefit from rewriting. The authors never formally define what they mean by \"fair,\" relying instead on examples. I would recommend adding a precise explanation when the sensitive attributes are introduced. Additionally, the paper's focus on spectral diffusion models is not emphasized enough, in my opinion.\n\nFinally, while the experiments are convincing, the code is not available, and neither the paper nor the appendix seems to provide enough detail to reproduce the results. For instance, I would not be able to reproduce the downstream task performance evaluation because no information on the GCN's characteristics is provided. For these reasons, the answers to questions 6 and 8 of the checklist should have been \"No,\" and a justification would have been welcome.\n\nQUESTIONS:\nEquation (5): The term $\\hat{A}^{dis}_0$ does not appear to be defined in the text.\n\nLine 219: It would be helpful to specify which groups are used to evaluate the structural fairness metric, $\\phi_{struct}$.\n\nLines 285 and 287: It seems there is a typo. s_theta should likely be z_theta.\n\nLine 406: The journal is missing from this reference.\n\nLine 499 (and Bibliography): The formatting for ICLR references is inconsistent throughout the bibliography.\n\n\nMy other questions and comments are detailed in the \"Weaknesses\" section above.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-16"}

comments: SUMMARY:
In this paper, the authors propose a novel graph generation framework named FairGEM, which can mitigate both graph structural bias and node feature bias simultaneously. Both theoretical analysis and empirical experiments verify the effectiveness of FairGEM.

STRENGTHS AND WEAKNESSES:
**strength**

1. Overall, this paper is well-written and easy to follow.

2. The proposed FairGEM is guaranteed with theoretical foundation.

3. Extensive experiments verify the effectiveness of FairGEM in fairness metrics.

**weakness**

1. The definitions of $\Delta _{DP}$ and $\Delta _{EO}$ are missing, which should be given in the appendix for better clarity.

2. What about the comparisons with some powerful fair GNN methods trained on the original graphs? Such as FairSIN [1], EDITS [2] and others.

3. Currently, the authors mainly focus on node classification in the downstream tasks. Taking link prediction tasks (like dyadic fairness) into considerations can better enhance the contributions of FairGEM compared with other fair GNN methods.

[1] FairSIN: Achieving Fairness in Graph Neural Networks through Sensitive Information Neutralization. AAAI 2024.

[2] EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks. WWW 2022.

QUESTIONS:
please refer to the weakness

LIMITATIONS:
The authors do not discuss the limitations of their work. However, as mentioned in the weakness, I believe that including experiments or empirical analysis on link prediction tasks can greatly improve the quality of this paper.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-29

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents FairGEM, a framework for generating synthetic graphs that aim to be both realistic and fair with respect to sensitive attributes. It introduces fairness-aware regularizers on both graph structure and node features, combines them with a disentanglement-based encoder using a VAE and HGR criterion, and provides theoretical guarantees relating fairness at generation time with downstream task bias. Experimental results show improved fairness across several benchmarks and metrics, including node classification accuracy and fairness gaps (ΔDP, ΔEO).\n\nSTRENGTHS AND WEAKNESSES:\nStrength\n\n* The paper tackles an important and timely problem: how bias in graph generation can influence downstream tasks, such as node classification.\n\n* It introduces a novel framework that adds fairness-aware regularizers to control both structural and feature-based biases.\n\n* The authors include theoretical results (Theorems 4.2 and 4.3) that support their claims (and proper theoretical proof in apendices), showing how bias in the generated graphs can affect performance in downstream models.\n\n* A key technical contribution is the use of Variational Autoencoders (VAEs) to separate sensitive-related features from unrelated ones. The approach also leverages diffusion models, which are claimed to avoid node ordering issues seen in autoregressive methods.\n\n* The method is evaluated on real-world datasets, and shows that it can improve fairness (lower ΔDP and ΔEO) while maintaining comparable accuracy to existing baselines.\n\nWeaknesses: \n* While the paper reads well, it does not clearly refer to figures or tables when making key arguments, which hurts clarity and traceability.\n\n* There are inconsistencies in the numbering of definitions and theorems: Definitions 4.2 and 4.3 are missing, while Theorems 4.2 and 4.3 are present. Since all of this occurs within Sections 4.1 to 4.3, it makes the structure harder to follow and disrupts the logical flow of the paper.\n\n* The ablation study is weakly developed. There’s little discussion, and it’s unclear how the conclusions connect to Figure 2.\n\n* The reported performance differences with other models are often small and lack statistical validation (e.g., no error bars, confidence intervals, or significance testing).\n\n* The study doesn’t clearly isolate the contribution of individual components (e.g., feature fairness vs. disentanglement). For example, although the paper promotes the use of diffusion models as a major strength, this aspect isn’t clearly reflected in the results or conclusions.\n\n* There is no qualitative or visual analysis to help the reader understand how the generated graphs differ in terms of fairness or structure.\n\n* Reproducibility is lacking: the paper does not provide implementation details, training configuration, or hyperparameter tuning strategies. There is no mention of code availability, runtime, or resource usage, which makes it hard to compare with other models in terms of practicality.\n\n* While ΔDP and ΔEO are standard metrics for fairness, the paper does not fully explain their relevance in the context of downstream node classification, or why they are appropriate indicators for this task.\n\nQUESTIONS:\n* In what way is your proposal a meaningful contribution, given that its performance is very similar to existing state-of-the-art methods?\n\n* What are the concrete benefits of using diffusion models over autoregressive ones, especially when no performance improvements are demonstrated?\n\n* Why does the paper omit implementation details of your model, including training setup and reproducibility information?\n\nLIMITATIONS:\nNo. Author do not mention any limitations on their approach.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 1, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

comments: SUMMARY:
This paper proposes a one-shot spectral diffusion framework with two fairness regularizers, (1) Structural regularizer that minimizes intra- vs inter-group edge-reconstruction disparity and (2) Feature regularizer that first disentangles sensitive-related vs sensitive-irrelevant attributes (via a VAE + HGR penalty) and then enforces MMD fairness only on the insensitive part. Theory insights

STRENGTHS AND WEAKNESSES:
Strength:

1. This paper addresses an under-explored but important fairness problem in graph generation, which is a interesting problem
2. This paper novelly integrates recent spectral diffusion methods, avoiding node-ordering issues.
3. The theoretical link between bias in eigen-value diffusion and downstream ΔDP is novel.

Weakness:

1. There is proof gaps: Theorem 4.2/4.3 rely on undefined constants (M, K, C, Σt) and assume Lipschitz/non-expansive GNN layers without verification.
2. Datasets are small/medium, method might struggle scaling to graphs with >100k nodes or multiple sensitive attributes.
3. Only binary sensitive variable considered; real-world use often needs multi-class or intersectional fairness.

QUESTIONS:
1. What is the computational cost compared to GSDM? Spectral diffusion on Pokec-z (68k nodes) could be heavy. 
2. Many fairness improvements are small (e.g., Fair-DD 0.020 vs 0.027). Are these differences statistically significant at p < 0.05?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 4
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-04