Title: Diffusion-Guided Graph Data Augmentation
Venue: NeurIPS 2025
Accepted: yes
Keywords: diffusion based graph data augmentation

node classification

link prediction

graph classification
Abstract: Graph Neural Networks (GNNs) have achieved remarkable success in a wide range of applications. However, when trained on limited or low-diversity datasets, GNNs are prone to overfitting and memorization, which impacts their generalization. To address this, graph data augmentation (GDA) has become a crucial task to enhance the performance and generalization of GNNs. 
Traditional GDA methods employ simple transformations that result in limited performance gains. Although recent diffusion-based augmentation methods offer improved results, they are sparse, task-specific, and constrained by class labels. In this work, we propose a more general and effective diffusion-based GDA framework that is task-agnostic and label-free.
For better training stability and reduced computational cost, we employ a graph variational auto-encoder (GVAE) to learn a compact latent graph representation. A diffusion model is used in the learned latent space to generate both consistent and diverse augmentations. 
For a fixed augmentation budget, our algorithm selects a subset of samples that would benefit the most from the augmentation.
To further improve performance, we also perform test-time augmentation, leveraged by the label-free nature of our method.
Thanks to the efficient utilization of GVAE and latent diffusion, our algorithm significantly enhances machine learning safety measures, including calibration, robustness to corruptions, and prediction consistency. Moreover, our method has shown improved robustness against four types of adversarial attacks and achieves better generalization performance. 
To demonstrate the effectiveness of the proposed method, we compare it with 30 existing methods on 12 benchmark datasets across node classification, link prediction, and graph classification in various learning settings, including semi-supervised, supervised, and long-tailed data distributions. 
The code will soon be made publicly available.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes Diffusion-Guided Graph Data Augmentation (D-GDA), a novel framework to enhance the performance and generalization of Graph Neural Networks (GNNs) by generating diverse and consistent graph augmentations. The key components of D-GDA include Target Sample Selector (TSS), Graph Variational Autoencoder (GVAE) and Latent Diffusion Model (LDM).\n\nThe method is task-agnostic and label-free, enabling test-time augmentation. Extensive experiments on 12 benchmarks show superior performance over 30 SOTA methods, with improvements in generalization, robustness to adversarial attacks, and ML safety measures.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The paper introduces a label-free, diffusion-based GDA framework applicable to multiple graph tasks, unlike prior task-specific methods.\n2. Neighborhood-aware conditioning ensures augmentations preserve local structure while diversifying data.\n\nWeaknesses:\n\n1. The paper claims to balance diversity and consistency in augmentations but does not explicitly define how these metrics are measured. Are they based on feature variance or some other criterion?\n2. While diversity and consistency are highlighted as key advantages, the paper does not explain why these metrics matter and whether they conflict (e.g., more diversity may reduce consistency).\n3. The TSS relies on a baseline GNN to identify \"difficult\" samples via entropy. However, the choice of baseline architecture (e.g., GCN or GraphSAGE) may bias sample selection. Is this evaluated? \n4. The paper claims lower computational costs due to latent-space diffusion but does not provide theoretical analysis or empirical benchmarks. Without these, the efficiency claims remain unsubstantiated. \n5. The method is described as using a Graph Variational Autoencoder (GVAE), but the cited work refers to Variational Graph Autoencoders (VGAE). Is this a terminological inconsistency or a modified architecture?\n\nQUESTIONS:\nSee weakness.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

comments: SUMMARY:
This paper proposes a Graph Data Augmentation (GDA) method called D-GDA, which is based on Graph Variational Autoencoder (GVAE) and Latent Diffusion Model (LDM). The framework consists of three key components: i). Target sample selection（TSS）, ii). Graph variational auto-encoder（GVAE）, and iii) Latent Diffusion.  In addition, D-GDA is model-agnostic and label-free, making it highly flexible for various applications. Extensive experiments demonstrate its effectiveness.

STRENGTHS AND WEAKNESSES:
Strengths:

1. The framework of D-GAD is sample and model-agnostic, enabling it enabling it to be applied more flexibly.

2. D-GDA not only focuses on performance improvement in the GDA scenario but also conducts research on model security.

3. The experiments compare performance across multiple tasks with numerous baseline models and ample benchmark datasets, providing comprehensive and thorough comparative analysis.

Weaknesses：

1. This paper lacks a structured theoretical description of TSS, GVAE, and LDM in the Methodology section(Sec. 3), which hinders a clear understanding of the technical approach.

2. GDA is inherently designed for scenarios with sparse or restricted graph data. However, LDM's convergence requires large amounts of data and significant computational resources. A theoretical proof of LDM's convergence in the current D-GDA setting is necessary.

3. D-GDA appears to primarily integrate existing techniques (e.g., GVAE, LDM) into the DGA scenario. The paper should further emphasize the description of its technical innovations.

QUESTIONS:
Please refer to the weaknesses.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-07-01

comments: SUMMARY:
This paper studies the research problem of graph data augmentation (GDA), with the aim of developing a task-agnostic GDA method. The authors proposed a diffusion-based approach, namely D-GDA. D-GDA has three main components: Target Sample Selector (TSS), a Graph Variational Autoencoder (GVAE), and a Latent Diffusion Model (LDM), where the diffusion happens on the latent space within the GVAE. Interestingly, with the help of TSS, the proposed method can sample the most valuable samples for augmentation when given a fixed data augmentation budget.

STRENGTHS AND WEAKNESSES:
s1. The paper overall is clearly written and easy to follow. 

s2. The authors conducted very thorough experiments over many benchmarks against many baseline methods. The experiments showcased the effectiveness of the proposed method.

s3. The design around augmenting with a fixed budget is interesting, and could be useful in real applications.

w1. While the authors argued around efficiency given the designs of TSS and test-time augmentation in the proposed method, the efficiency is still concerning considering the method design. There were also no experiments such as run-time comparison of the proposed method against baselines. Such evaluation, especially on larger scale benchmarks, would help showcasing on this perspective.

QUESTIONS:
I'm mainly curious about how actually this method scales, as well as how efficient it is.

LIMITATIONS:
see the weakness and question above.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors introduce a new framework for graph augmentation, applicable to all common graph tasks of graph classification, link prediction and node classification. It is based on three main components 1) target sample selector, that selects nodes/edges to augment 2) a GVAE that encodes/decodes the graph to a latent space and 3) a latent diffusion model that augments the embeddings and thus the graph. The method is tested on common benchmarks for this three tasks and shows improvement over other augmentation techniques. It also helps model robustness.\n\nSTRENGTHS AND WEAKNESSES:\nTraditional graph augmentation techniques have historically been lacking. And to make them successful you needed to make them domain specific. Using a generative approach is a neat way to avoid having to hand-craft them. The three components introduced are all quite sensible and follow current best generative model approaches (latent diffusion). The TSS using a baseline model to find hard nodes is nice and sensible idea, that is ablated/shown to work with in-silico experiments. In general the paper provides extensive experimental evidence and the results are very good. In both making the downstream GCN more robust and more accurate.\n\nThe method uses neighborhood embeddings as conditioning when denoising the augmented node. For graph classification the most important nodes are taken to be highest degree nodes. Both are sensible choices, but conceptually they do correlate with homophily, and most datasets used (especially link prediction and node classification ones) are quite homophilous. It would be interesting to know if improvements over base GNN correlate with how homophilous the dataset is, or if the method is robust to this.\n\nAs I understand test time augmentation is used in all experiments. It does make the method much more expensive than other competitors and judging by table 14 it is a crucial part of methods performance. This however is quite similar to model ensambling, so it would be good to see how it stacks up with simple model ensambling and/or what the results would be for the rest of the datasets (link prediction and graph classification) if no test time augmentation would be used, as computational expense wise that's closer to the baselines.\n\nQUESTIONS:\nPlease see the last two paragraphs (weaknesses) above. \n\nOne additional question is did you consider other node importance selection strategies for graph classification. Like page rank or just random selection. Degree-based selection certainly makes sense intuitively, but it would be nice to know if it's indeed the optimal choice.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}