Title: Dual Prototype-Enhanced Contrastive Framework for Class-Imbalanced Graph Domain Adaptation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Graph Domain Adaption

Class-Imbalanced Learning
Abstract: Graph transfer learning, especially in unsupervised domain adaptation, aims to transfer knowledge from a label-abundant source graph to an unlabeled target graph. However, most existing approaches overlook the common issue of label imbalance in the source domain, typically assuming a balanced label distribution that rarely holds in practice. Moreover, they face challenges arising from biased knowledge in the source graph and substantial domain distribution shifts. To remedy the above challenges, we propose a dual-branch prototype-enhanced contrastive framework for class-imbalanced graph domain adaptation in this paper. Specifically, we introduce a dual-branch graph encoder to capture both local and global information, generating class-specific prototypes from a distilled anchor set. Then, a prototype-enhanced contrastive learning framework is introduced. On the one hand, we encourage class alignment between the two branches based on constructed prototypes to alleviate the bias introduced by class imbalance. On the other hand, we infer the pseudo-labels for the target domain and align sample pairs across domains that share similar semantics to reduce domain discrepancies. Experimental results show that our ImGDA outperforms the state-of-the-art methods across multiple datasets and settings. The code is available at: https://github.com/maxin88scu/ImGDA.


Peer reviews:
comments: SUMMARY:
This paper proposes a dual prototype-enhanced contrastive framework, ImGDA, which focuses on the challenge of class imbalance in graph domain adaptation. The goal is to transfer imbalanced label knowledge from a labeled source graph to an unlabeled target graph.

STRENGTHS AND WEAKNESSES:
Strengths:
This work is relatively innovative, with a well-organized and coherent structure.

Weaknesses:
Some technical details are insufficiently addressed, which impact the overall reliability of the proposed method.

QUESTIONS:
1. In Section 3, the imbalance factor (IF) ρ is defined as N1/NC. What is the rationale behind this definition? When there are multiple tail classes, this formulation may not adequately reflect the degree of imbalance in the sample distribution. In the experimental setup, what is the ratio between head and tail classes?

2. The Cross-Domain Prototype Contrast relies on pseudo-labels in the target graph. How is the reliability of these pseudo-labels evaluated or ensured?

3. While Anchor-Based Prototype Construction may help alleviate class imbalance in the source graph, the Prototype-Enhanced Contrastive Learning still depends on pseudo-labels in the target graph. When the target domain also suffers from class imbalance, the pseudo-labels are likely to be biased toward head classes. Under such conditions, is the proposed method still robust and reliable?

LIMITATIONS:
The authors have discussed the limitations and potential negative societal impact of their work
TITLE: Official Review
CLARITY: 2
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-14

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents ImGDA, a dual-branch prototype-enhanced contrastive framework for unsupervised class-imbalanced graph domain adaptation that captures both local and global structure to generate class-specific prototypes from a distilled anchor set. It leverages cross-branch prototype contrast to rebalance and align class semantics within the source graph, and cross-domain prototype contrast—with non-parametric pseudo-labels and an adaptive temperature mechanism—to reduce distribution shifts and mitigate imbalance during training. Extensive experiments across multiple real-world graph datasets show that ImGDA consistently outperforms SOTA domain adaptation and imbalanced learning baselines.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- The proposed setting that integrates class imbalance with unsupervised graph domain adaptation is novel and addresses real-world distribution shifts overlooked by prior work.\n-\tThe dual-branch prototype-enhanced contrastive framework is effective at capturing both local and global semantics to rebalance source classes and align representations across domains.\n-\tExtensive experiments on multiple real-world datasets demonstrate ImGDA’s consistent superiority over SOTA baselines.\n\nWeaknesses:\n-\tThe authors only compare against standalone domain‐adaptation and standalone imbalance methods—intuitively, such methods perform poorly on tasks that combine domain shift with class imbalance—without evaluating combinations of domain‐adaptation GNNs with mature imbalance techniques, many of which can be integrated via simple plug-in loss functions or sampling strategies without any architectural changes.\n-\tSome recent baselines are missing, including the graph domain adaptation methods Pair-Align [1] and TDSS [2], as well as the graph imbalance learning method ConsisGAD [3].\n-\tAlthough the paper reports empirical efficiency metrics for ImGDA, it lacks a formal time and space complexity analysis to clarify how runtime and memory scale with graph size, class count, and anchor set.\n\n[1] Shikun Liu, Deyu Zou, Han Zhao, and Pan Li. \"Pairwise Alignment Improves Graph Domain Adaptation.\" ICML 2024.\n[2] Wei Chen, Guo Ye, Yakun Wang, Zhao Zhang, Libang Zhang, Daixin Wang, Zhiqiang Zhang, and Fuzhen Zhuang. \"Smoothness really matters: A simple yet effective approach for unsupervised graph domain adaptation.\" AAAI 2025.\n[3] Nan Chen, Zemin Liu, Bryan Hooi, Bingsheng He, Rizal Fathony, Jun Hu, and Jia Chen. \"Consistency training with learnable data augmentation for graph anomaly detection with limited supervision.\" In The Twelfth International Conference on Learning Representations. 2024.\n\nQUESTIONS:\nSee Weaknesses.\n\nLIMITATIONS:\nSee Weaknesses.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper addresses an important problem of class imbalance in unsupervised graph domain adaptation (GDA). The proposed ImGDA framework integrates dual-branch encoders, prototype construction, and contrastive learning to mitigate source-domain bias and domain shifts. While the approach is technically sound and well-structured, significant concerns regarding motivation, novelty, and experimental design limit its contribution.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n1. Clear Formulation: The problem setup (Section 3) and modular framework (Section 4) are well-presented.\n\n2. Theoretical Grounding: The theoretical analyses of the prototype contrastive loss (Theorems 4.1–4.2) provide a solid foundation.\n\nWeaknesses\n\n1. Insufficient Motivation and Empirical Analysis\n\nThe claim that class imbalance \"inevitably leads to knowledge extraction bias\" in GDA lacks robust empirical validation. The authors rely on a synthetic example (Figure 1) without demonstrating the prevalence of this issue in real-world graph datasets. Recommend to provide a thorough analysis of class imbalance in established graph benchmarks to quantify its impact on existing GDA methods. Statistical evidence would strengthen the motivation.\n\n2. Limited Methodological Novelty\n\nPrototype-based contrastive learning and anchor-based graph construction are well-studied in non-graph domains (e.g., CV/NLP) and recent graph learning. The paper does not clarify how ImGDA fundamentally advances these concepts for graphs. No graph-specific innovations (e.g., handling heterophily, noisy edges) are discussed. Recommend to explicitly differentiate ImGDA from prior work:\nHow do the \"prototype-enhanced\" losses uniquely address graph structural biases?\n\n3. Weak Experimental Design\n\nSynthetic Imbalance: Artificially inducing imbalance on balanced datasets (ACM/DBLP) does not reflect real-world scenarios. Performance on naturally imbalanced graphs (e.g., social networks with skewed communities) remains unverified.\n\nSmall-Scale Datasets: Experiments are limited to citation graphs. Larger heterogeneous graphs[1] can be also used in graph domain adaption task and should be included to test scalability and generalizability.\n\n[1] Jiang X, Jia T, Fang Y, et al. Pre-training on large-scale heterogeneous graph[C]//Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining. 2021: 756-766.\n\nQUESTIONS:\nSee Weaknesses.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 1, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the problem of graph unsupervised domain adaptation and aims to address the problem of class imbalance, which has been overlooked by prior methods. To overcome this, the authors propose the Dual Prototype-Enhanced Contrastive Framework (ImGDA). ImGDA consists of (1) dual-branch embeddings that simultaneously learn local and global structure information, (2) anchor-based prototype construction that considers the balance between classes, and (3) prototype-aware contrastive learning that simultaneously performs class alignment and domain alignment, and aims to mitigate both class imbalance and domain difference problems.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. The performance is very good overall, and it is especially impressive that the performance remains stable for a large imbalance factor ρ, with a large performance gap to the baseline.\n2. Visualizations and various ablation experiments are used to intuitively demonstrate the effectiveness of the proposed method.\n\nWeaknesses\n1. In Figure 2 (Main Framework), there is no alignment between the caption and the visual elements, making it difficult to intuitively understand which parts of the figure are referred to by the components described in the text.\n2. Section 4.3 claims that aligning prototypes between local and global branches contributes to performance improvements, but it lacks an explanation of how this alignment relates to mitigating the class imbalance problem. It would be better to see some rationale or intuitive interpretation of why simply aligning the two branches improves the tail class representation.\n3. In Figure 4, the w/o local and w/o global settings seem to remove one of the two branches required for computing L_{cb}; wouldn't this make the use of L_{cb} infeasible in those cases?  It is somewhat surprising that these settings outperform the w/o L_{cb}, and require further explanation of experimental conditions.\n4. While the degree of class imbalance in the source domain is clearly controlled by the imbalance factor ρ, it is not specified whether the target domain has a balanced or equally unbalanced distribution. To reflect a more practical scenario, the paper would benefit from a discussion or empirical results on whether the proposed method remains effective when the target domain is also class-imbalanced.\n\nQUESTIONS:\nSee the weakness section.\n\nLIMITATIONS:\nThe limitations of the proposed method are described in the Appendix. E (Broader Impact and Limitations).", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}