Title: GD$^2$: Robust Graph Learning under Label Noise via Dual-View Prediction Discrepancy
Venue: NeurIPS 2025
Accepted: yes
Keywords: Graph Neural Network

label noise
Abstract: Graph Neural Networks (GNNs) achieve strong performance in node classification tasks but exhibit substantial performance degradation under label noise. Despite recent advances in noise-robust learning, a principled approach that exploits the node-neighbor interdependencies inherent in graph data for label noise detection remains underexplored. To address this gap, we propose GD$^2$, a noise-aware \underline{G}raph learning framework that detects label noise by leveraging \underline{D}ual-view prediction \underline{D}iscrepancies. The framework contrasts the \textit{ego-view}, constructed from node-specific features, with the \textit{structure-view}, derived through the aggregation of neighboring representations. The resulting discrepancy captures disruptions in semantic coherence between individual node representations and the structural context, enabling effective identification of mislabeled nodes. Building upon this insight, we further introduce a view-specific training strategy that enhances noise detection by amplifying prediction divergence through differentiated view-specific supervision. Extensive experiments on multiple datasets and noise settings demonstrate that \name~achieves superior performance over state-of-the-art baselines.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nAuthors present GD2, which is a GNN training framework for node classification designed to be robust against mislabeled nodes. It uses a Gaussian Mixture Model to identify noisy samples by looking at discrepancies between individual node features and the aggregated features around the nodes. They call disagreements between these two \"views\" as the dual-view prediction discrepancy. Authors show experimental results across six datasets, and generally show improvements over existing baselines.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: the dual-view mechanism seems intuitive, and grounded in (some) theory. The experiments are well-designed, using different levels of noise to simulate mislabeled nodes, then checking to see how different models react. Some decent ablations as well to help readers understand the importance of individual components in GD2. Overall, the implementation doesn't seem too heavyweight to apply to existing GNNs as well.\n\nWeaknesses: my main reservation with this paper is that discrepancies in labels between the two views could be an outcome of heterophily in the graph. I would like to see more discussion and/or theory behind the specific gains on heterophilic graphs. Perhaps this ties in with the heterogeneous graphs, which the authors have acknowledged as one limitation. Lastly, performance gains are minor, albeit consistent. Usually <~5% relative performance gain.\n\nQUESTIONS:\nThough the authors run experiments on two heterophilic graphs, I struggle to understand why GD2 would work well here. Wouldn't the model expect different labels between any ego node and its structural neighborhood?\n\nThere is quite some sensitivity to tau selection. Optimal value will also depend on the dataset. Is there some adaptive selection technique authors would recommend that might be useful on top of grid search?\n\nHow would GD2 perform when there is class imbalance? Presumably the GMM learns noise due to a healthy distribution of labels. Would it incorrectly think minority class samples are noisy?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces GD², a framework for addressing label noise in Graph Neural Networks for node classification. The core idea is to detect noisy labels by leveraging the prediction discrepancy between two distinct views of a node: an \"ego-view,\" constructed from the node's own features, and a \"structure-view,\" derived from the representations of its neighborhood. The framework is further enhanced by a view-specific training strategy, which uses a GMM to identify a set of clean nodes and then generates pseudo-labels for a subset of the noisy nodes. By feeding different supervisory signals (original noisy labels vs. purified pseudo-labels) to the different views, the framework amplifies the discrepancy signal.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1.\tThe paper tackles the problem of label noise, which is a significant challenge in real-world applications of GNNs. Developing methods that improve robustness is of high practical value.\n2.\tThe view-specific training strategy is clever: rather than passively observing the discrepancy between views, the framework proactively amplifies this signal. By feeding original noisy labels to the ego-view and purified labels to the structure-view, the method intentionally creates a larger divergence for mislabeled nodes.\n3.\tAs reported, the proposed GD² framework achieves state-of-the-art performance, outperforming several baselines across multiple datasets and under various noise settings.\n\nWeaknesses \n1.\tThe method's core premise is that a discrepancy between the ego-view and structure-view indicates a noisy label, which is based on the homophily assumption. In Heterophily graphs, where connected nodes are often of different classes, this assumption may be violated. For example, for fraudulent and legitimate users, a correctly labeled node in such a graph could be misidentified as noisy. More discussion on how the framework adapts to Heterophily graphs should be included.\n2.\tThe paper should further discuss the framework's limitations under extremely high noise ratios (e.g., p > 0.5). At such levels, the neighborhood information becomes unreliable, and the GMM may struggle to find a clean separation between the clean and noisy nodes. The experimental results also suggest that the method’s performance advantage over baselines may diminish under high-noise regimes.\n3.\tThe GD² framework is complex. Each epoch involves computing three distinct views, calculating discrepancies for all training nodes, fitting a GMM, and optimizing three separate loss functions. This introduces significant computational and time overhead. The authors should include an analysis of the computational complexity and runtime costs.\n\nQUESTIONS:\n1.\tHow does the method perform on heterophily graphs where the assumption of neighbor similarity may be violated?\n2.\tWhat are the performance limitations of the framework under extremely high noise ratios?\n3.\tWhat is the computational complexity and runtime of the GD² framework?\n\nLIMITATIONS:\nThe authors discuss the limitation that their work is restricted to static graphs.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn order to mitigate noises in the graph deep learning without i.i.d. node labels, the paper introduces a method to identify the nodes with nosy labels by measuring the distribution of ego-view representation and structure-view representation. The proposed framework then proposes pseudo-labels for the noisy nodes for accurate nose prediction. The paper also validates the method in theory. Finally, the authors evaluated the method on multiple datasets with different distributions and raios of noisy labels.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The paper proposes a novel approach to identify nodes with noisy labels based on the distribution discrepancy between the ego-view and the structure-view.\n\n2. The paper conducted thorough experiments with both various noise ratio and noise distribution.\n\nWeakness:\n\n1. The paper should clarify how the interdependencies among connected nodes in graph affect the existing noise-agnostic works. To the best of my knowledge, NRGNN also took the neighborhood of nodes with similar labels into consideration and connected the labeled and unlabeled nodes for noise-agnostic learning.\n\n2. The notations of the papar should be reorganized. For example, ‘s’ is redefined as clean-label component in line 158 while it was the the footnote for structure-view in the notations above.\n\n3. The main assumption and theoretical justification for now seems not sound for me. First, take the experiment with p = 0.6 as an example. Emperically, for clean-labeld node with 60% noisy neighbors, I believe there would still be distribution discrepancy between the ego-view and the structure-view. In this way, it cannot be taken as an identification of nodes with noisy labels. Second, for the theoretical justification, I blieve the main suppose the authors should prove is that the the distribution discrepancy should exists and only exists in nodes with noisy labels. However in the proving procedure, the result only provides the upper bound of probability of distribution discrepancy, but does provide any direct correlation between the distribution discrepancy the whether the label of the node is noisy or not.\n\n4. Also, as the authors mention that “the structure-view captures topological patterns that remain relatively stable under noise”, why not directly utilize the structure-view as clean representation for node label prediction? \n\n5. The improvement on the results of the provided experiment seem marginal, with only less than 1% accuracy improvement. In this way, the proposed method seems not effective enough for handling the noisy labels of graph nodes.\n\nQUESTIONS:\n1. Please clarify the narrative problems mentioned in the weakness part above.\n2. Please further conduct ablation study that only utilize the structure-view as clean representation for node label prediction in both training and inference.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 1, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-30"}

comments: SUMMARY:
The paper proposes GD2, a robust graph learning framework for noisy data. The model utilizes two distinct graph views, the ego-view and the neighbor-view, and their discrepancy, to filter out wrongly labeled data and relabel them during training to achieve robust training.

STRENGTHS AND WEAKNESSES:
Strengths:

1. The method is novel and is theoretically guaranteed.
2. The empirical result is strong and gives good performance. 
3. The paper is clear in explaining the method. 

Weaknesses:

1. It is unclear how the model can reliably distinguish clean vs noisy data at the beginning of training, when both ego- and neighbor-view predictions may be unstable. 

2. The experiments are conducted on a small number of benchmarks, lacking large-scale datasets [1].

3. The method is only validated with GCNs; it is unknown whether the approach remains effective when applied to other widely used GNN architectures like GAT, GraphSAGE, or transformer-based models.

4. The clean node threshold (τ) varies across datasets, but the paper does not offer a principled or simple strategy for choosing it automatically.

[1] Lim, Derek, et al. "Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods." *Advances in neural information processing systems* 34 (2021): 20887-20902. datasets are too few

QUESTIONS:
See weaknesses.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-02