Title: L2DGCN: Learnable Enhancement and Label Selection Dynamic Graph Convolutional Networks for Mitigating Degree Bias
Venue: NeurIPS 2025
Accepted: yes
Keywords: Missing Data

few-Label Learning

graph convolutional networks (GCNs)

graph representation learning
Abstract: Graph Neural Networks (GNNs) are powerful models for node classification, but their performance is heavily reliant on manually labeled data, which is often costly and results in insufficient labeling. Recent studies have shown that message-passing neural networks struggle to propagate information in low-degree nodes, negatively affecting overall performance. To address the information bias caused by degree imbalance, we propose a Learnable Enhancement and Label Selection Dynamic Graph Convolutional Network (L2DGCN). L2DGCN consists of a teacher model and a student model. The teacher model employs an improved label propagation mechanism that enables remote label information dissemination among all nodes. The student model introduces a dynamically learnable graph enhancement strategy, perturbing edges to facilitate information exchange among low-degree nodes. This approach maintains the global graph structure while learning graph representations. Additionally, we have designed a label selector to mitigate the impact of unreliable pseudo-labels on model learning. To validate the effectiveness of our proposed model with limited labeled data, we conducted comprehensive evaluations of semi-supervised node classification across various scenarios with a limited number of annotated nodes. Experimental results demonstrate that our data enhancement model significantly contributes to node classification tasks under sparse labeling conditions.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the degree bias problem in node classification when there are insufficient labeled nodes. The authors demonstrate that the proposed method enhances graph structures by focusing on low-degree nodes, enabling effective information propagation and significantly improving performance on various homogeneous and heterogeneous graphs.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths \n1. The article has a complete structure and clear writing style. \n2. Some experimental results seem promising.\n### Weaknesses\n1. The authors should analyze the potential drawbacks of the proposed method to provide readers with a more comprehensive understanding of the solution. \n2.Table 2 lists the parameter ranges but does not specify how the optimal values were selected (e.g., whether grid search or Bayesian optimization was used). It is recommended to supplement the description of \"hyperparameter optimization strategies\" in Section 4.2. \n3.Some recent models related to mitigating degree bias have emerged, but they are missing from the comparative experiments. \n4. The improved model mentioned in the paper is a self-training model, which may lead to higher time complexity. However, the paper lacks an analysis of time complexity.\n\nQUESTIONS:\n1. The authors should analyze the potential drawbacks of the proposed method to provide readers with a more comprehensive understanding of the solution. \n2. Table 2 lists the parameter ranges but does not specify how the optimal values were selected (e.g., whether grid search or Bayesian optimization was used). It is recommended to supplement the description of \"hyperparameter optimization strategies\" in Section 4.2. \n3. Some recent models related to mitigating degree bias have emerged, but they are missing from the comparative experiments. \n4. The improved model mentioned in the paper is a self-training model, which may lead to higher time complexity. However, the paper lacks an analysis of time complexity.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-21"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nTo address the challenges of labeling costs and low-degree node propagation in GNN node classification, the L2DGCN model is proposed. Its teacher-student architecture solves the problems of long-range information transmission and low-degree node interaction through improved label propagation and dynamic graph augmentation strategies, respectively. By integrating a label selector to optimize the learning process, the model significantly enhances performance in semi-supervised classification experiments with limited labeled data.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n1. It solves a major challenge in graph neural networks, namely degree bias in semi-supervised node classification, and proposes an innovative method to address this problem. I appreciate the depth of your analysis, the clarity of the methodology, and the extensive experiments you conducted to validate the model. Your combination of pseudo-label propagation and dynamic graph augmentation is a promising direction, and the improvements reported on various datasets are commendable. \n2. Validating node classification performance in 3 low-label-rate scenarios on 6 datasets is thorough and extensive.\n\n**Weaknesses**\n1. This work addresses the degree bias problem in graph convolutional networks, which has also been widely studied in previous works. The authors are required to enrich the Related Work section. Below are some papers related to solving degree bias for the authors' reference.\n\n[1] Van Thuy Hoang, Hyeon-Ju Jeon and  O-Joun Lee. \"Mitigating Degree Bias in Graph Representation Learning With Learnable Structural Augmentation and Structural Self-Attention,\" IEEE Transactions on Network Science and Engineering, 2025, doi: 10.1109/TNSE.2025.3563697.\n\n[2] Mingxia Zhao, Adele Lu Jia, “DAHGN: Degree-Aware Heterogeneous Graph Neural Network,” Knowledge-Based Systems, 2014, 285: 111355.\n\n[3] Mingxuan Ju, Tong Zhao, Wenhao Yu, Neil Shah, and Yanfang Ye. 2023. GRAPHPATCHER: mitigating degree bias for graph neural networks via test-time augmentation. In Proceedings of the 37th International Conference on Neural Information Processing Systems (NIPS '23). pp. 55785–55801.\n\n2. In Section 4.2, K is fixed at 10. The correlation between this value and the dataset scale or node degree is not clarified. It is recommended to add an experiment on \"the correlation between K and model performance\" in the parameter analysis of Section 4.5. \n\n3. Formula (18) includes the contrastive loss $L_{CL}$, but its individual impact is not tested in the ablation experiments. It is suggested to add a control group in Figure 3 that \"removes only $L_{CL}$\" to clarify its improvement ratio on classification accuracy.\n\nQUESTIONS:\nPlease refer to the above weakness.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a new model, L2DGCN, to address the challenge of semi-supervised node classification on graphs with very few labeled nodes. The authors identify \"degree bias\" as a primary cause for performance degradation in this setting. The model consists of a teacher-student architecture.\n\nSTRENGTHS AND WEAKNESSES:\n# Strength \n\n* The paper is motivated by the degree bias problem in GNNs, which is well recognized in the literature.\n* The authors have conducted a comprehensive set of experiments across six benchmark datasets and multiple low-data settings.\n* The proposed L2DGCN framework is modular, integrating several popular techniques like teacher-student learning, label propagation, graph enhancement, and self-training, which are relevant to the problem at hand.\n\n# Weakness\n\n* The paper's primary motivation is to mitigate the negative effects of degree bias on low-degree nodes, which suffer from insufficient information propagation. However, the proposed \"Edge Pruning Based on Node Degrees\" component explicitly states that it retains edges for high-degree nodes while removing edges connected to low-degree nodes.  This seems to be in direct contradiction with the stated goal of the paper.\n* Individual component (self-training with pseudo-labels, soft label propagation, learnable structure augmentation, contrastive loss, and pseudo-label selection) is based on existing techniques. The novelty of each component is limited, and their integration feels more like an ad-hoc ensemble than a principled, unified model.  Besides, it has a large number of sensitive hyperparameters.\n* The effectiveness of the label selector depends on the accuracy of soft pseudo-label assignment in ultra-low label regimes. Despite the claimed robustness, quantitative evidence for reduction in label noise or comparisons with alternative confidence estimation approaches are absent.\n* While the ablation study (Figure 3) quantitatively shows the contributions of label selection and graph enhancement, there is little qualitative or visualization analysis regarding how graph structure actually changes and how degree bias is mitigated in practice\n* Some related works about under-reaching issue should also be inclued [1,2,3,4].\n\nRefs:\n[1] Position-aware structure learning for graph topology-imbalance by relieving under-reaching and over-squashing, CIKM 2022.\n[2] NodeMixup:  Tackling Under-Reaching for Graph Neural Networks, AAAI 2024.\n[3] Rewiring with positional encodings for graph neural networks.\n\nQUESTIONS:\nPlease address Weakness part.\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

comments: SUMMARY:
The paper focuses on addressing the performance issues of graph neural networks (GNNs) in low-label data and low-degree node scenarios, proposing the L2DGCN model. The model employs a teacher model to improve label propagation for cross-node remote information interaction and a student model to dynamically enhance graph structures, promoting communication among low-degree nodes while maintaining topology. A label selector is used to optimize pseudo-labels. Experiments validate its effectiveness in mitigating degree bias and improving performance in low-label scenarios.

STRENGTHS AND WEAKNESSES:
Strengths 
1. The method is innovative, proposing a dual-model architecture that combines decoupled GCN and dynamic graph enhancement techniques to specifically address the information propagation challenges of low-degree nodes. 
2. The motivation behind the proposed model is well-founded, and the experimental design is robust. It is validated in low-label scenarios across multiple different types of datasets, with results showing that the accuracy outperforms 14 baseline methods, demonstrating the model's effectiveness.

Weaknesses

1. In Section 4.2, the value range of $\mu$ in Table 2 is inconsistent with that in Figure 4 of Section 5.4. Please carefully check and verify. 
2. Generalizability of the label selector: The setting of the confidence threshold $\mu$  relies on dataset characteristics. It is recommended to add comparative results with (  $\mu$=0 ) (full pseudo-labels) in the ablation experiment of Section 4.4 to validate the necessity of the filtering mechanism.

QUESTIONS:
For questions, please see the weakness in the above section.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 4
IMPACT: 4
ORIGINALITY: 3
DATE: 2025-07-02