Title: Towards Unsupervised Open-Set Graph Domain Adaptation via Dual Reprogramming
Venue: NeurIPS 2025
Accepted: yes
Keywords: Graph Neural Networks

Unsupervised Graph Domain Adaptation

Open-set Graph Domain Adaptation
Abstract: Unsupervised Graph Domain Adaptation has become a promising paradigm for transferring knowledge from a fully labeled source graph to an unlabeled target graph. Existing graph domain adaptation models primarily focus on the closed-set setting, where the source and target domains share the same label spaces. However, this assumption might not be practical in the real-world scenarios, as the target domain might include classes that are not present in the source domain. In this paper, we investigate the problem of unsupervised open-set graph domain adaptation, where the goal is to not only correctly classify target nodes into the known classes, but also recognize previously unseen node types into the unknown class. Towards this end, we propose a novel framework called GraphRTA, which conducts reprogramming on both the graph and model sides. Specifically, we reprogram the graph by modifying target graph structure and node features, which facilitates better separation of known and unknown classes. Meanwhile, we also perform model reprogramming by pruning domain-specific parameters to reduce bias towards the source graph while preserving parameters that capture transferable patterns across graphs. Additionally, we extend the classifier with an extra dimension for the unknown class, thus eliminating the need of manually specified threshold in open-set recognition. Comprehensive experiments on several public datasets demonstrate that our proposed model can achieve satisfied performance compared with recent state-of-the-art baselines. Our source codes and datasets are publicly available at https://github.com/cszhangzhen/GraphRTA.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the problem of unsupervised open-set graph domain adaptation, where the goal is not only to correctly classify target nodes belonging to known classes but also to detect previously unseen node types as unknown. The authors propose a framework called GraphRTA, which performs reprogramming on both the graph and model levels. Specifically, they modify the target graph structure and node features to improve the separation of known and unknown classes. Domain-specific parameters are pruned to reduce bias toward the source graph while retaining transferable patterns. The classifier is extended with an additional dimension to represent the unknown class.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The paper proposes a new solution for open-set domain adaptation (OSDA) in graph neural networks, explicitly distinguishing between known and unknown node classes.\n\n2. The overall writing is generally clear and fluent, and the algorithm is logically presented.\n\n3. Experimental results demonstrate improved performance compared to existing methods.\n\n\nWeaknesses:\n\n1. Some parts of the paper still remain unclear, which makes it difficult to fully understand the core ideas. For example, in the second paragraph of Section 3.3, the use of M as a mask in the node representation is not well explained. It appears to be a learnable parameter, yet the following text mentions that weights with small gradients with respect to M are masked. This suggests M might instead be derived from another process. The explanation needs clarification.\n\n2. In the same section, the hyperparameter \\rho is mentioned, but its definition and role in the model are not clearly described. Additionally, a dynamic adaptive threshold is referenced but not sufficiently explained. Since this is claimed as a key contribution, it should be fully justified and elaborated.\n\n3. On line 200, it is unclear how \\delta A is computed. If it is a learnable parameter, how is the binary matrix learned? Also, the choice of the budget B is not discussed. This seems like an important hyperparameter that could significantly impact performance, so its value, how to choose, and default setting should be explained in more detail.\n\n4. The paper employs a Beta mixture model, which appears nearly identical to the one proposed in \"Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation\". Although the authors cite this work in the introductory description at the beginning of the paragraph, they should explicitly acknowledge the similarity in this section, as the techniques are almost the same.\n\n5. While the proposed method shows strong performance in most scenarios in Tables 2 and 3, it would be helpful to discuss the cases where the method does not achieve the best results, and explore possible reasons why.\n\nQUESTIONS:\nQ1: How is the value of the budget B determined?\n\nQ2: How is the learning of \\delta A achieved in practice?\n\nAdditionally, my current evaluation assumes that the authors will successfully address the concerns and confusions mentioned in the weaknesses section, so I would appreciate it if the authors could also respond to the questions raised there.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-25"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper studies unsupervised open-set graph domain adaptation. The authors claim that the target domain might include classes not in the source domain in the real-world scenarios. To address this issue, the authors propose to not only classify target nodes into the known classes but also classify unseen node types into the unknown class. Specifically, a novel framework is proposed to conduct reprogramming on both the graph and model sides. For model reprogramming, the authors prune domain specific parameters to reduce bias. For graph reprogramming, the authors reprogram the graphs to facilitate better separation of known and unknown classes. Results on several public datasets show that the proposed model can outperform recent baselines with different gains.\n\nSTRENGTHS AND WEAKNESSES:\nPros:\n1.\tThis paper investigates open-set graph domain adaptation, which is interesting and less explored in the community.\n2.\tDifferent types of datasets (citation, ogbn-arxiv and WebKB) are utilized for evaluation.\n3.\tComprehensive results demonstrate that its performance is promising.\nCons:\n1.\tThe authors should provide a more detailed introduction to the datasets.\n2.\tOne of the recent baselines is not discussed and compared.\n3.\tMore ablation studies should be given. What if we use entropy threshold for open-set detection?\n\nQUESTIONS:\n1.\tThe authors should provide a more detailed introduction to the datasets used in the experiments. For example, it would be helpful to clarify how open-set classes are selected within each dataset.\n2.\tOne of the recent baselines appears to be missing from the discussion and experimental comparison. Including this baseline would provide a more complete and fair evaluation of the proposed approach. Please refer to reference [1].\n3.\tAdditional ablation studies would strengthen the paper by providing deeper insights into the effectiveness of the proposed method. For example, it would be helpful to explore alternative strategies for open-set detection—such as using an entropy-based threshold—and compare their performance with the proposed approach.\n4.\tSome of the ablation studies currently placed in the appendix are crucial for understanding the core contributions of the paper and should be moved to the main content.\nReferences:\n[1] Shen X, Chen Z, Pan S, et al. Open-Set Cross-Network Node Classification via Unknown-Excluded Adversarial Graph Domain Alignment[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2025, 39(19): 20398-20408.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the problem of unsupervised open-set graph domain adaptation (UOSGDA), where the target graph contains classes unseen in the source domain. The authors propose GraphRTA, a dual-reprogramming framework that modifies both graph structure/node features (\"graph reprogramming\") and prunes domain-specific model parameters (\"model reprogramming\"). Key innovations include: (1) Joint graph and model adaptation to separate known/unknown classes; (2) An extended classifier with an additional \"unknown\" dimension to avoid manual thresholding. Experiments on public datasets demonstrate superiority over GNNs, closed-set GDA, and open-set graph learning baselines.\n\nSTRENGTHS AND WEAKNESSES:\nQuality:\nStrength: Experimental design is rigorous, covering diverse baseline types (GNNs, closed-set GDA, open-set methods) across multiple datasets.\nWeakness: Model complexity raises concerns. The combination of adversarial training, multiple loss functions, and dual reprogramming may lead to training instability and convergence difficulties.\n\nClarity:\nStrength: The manuscript is well-structured and clearly written, with logically motivated components.\n\nSignificance and Originality:\nWeakness: While UOSGDA is a relevant problem, the technical novelty is limited. Core techniques (entropy-based known/unknown separation, adversarial domain alignment) are adaptations of existing methods from non-graph open-set domains. The incremental contribution does not sufficiently advance the state-of-the-art.\n\nQUESTIONS:\n1.Potential of LLM Integration: Given datasets with text-describable nodes (e.g., Citation networks), have you considered leveraging LLMs for unknown class detection? LLMs could generate semantic explanations for classification decisions and handle the open-set classification.\n\n2.There is an inconsistency in reported baselines: Table 4 cites GCN HS metric as 56.91%, while Table 2 lists as 59.41%. Please verify and correct all numerical results.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the task of unsupervised open-set graph domain adaptation, where the target domain may contain classes do not present in the source domain, an important consideration in real-world applications. To tackle this challenge, the authors propose a novel framework that simultaneously classifies target nodes into known categories while identifying unseen node types as belonging to an unknown class. The approach involves reprogramming at both the model and graph levels. Specifically, model reprogramming is achieved by pruning domain-specific parameters to mitigate bias, while graph reprogramming adjusts graph structures to enhance the separation between known and unknown classes. Experimental results on multiple public datasets demonstrate that the proposed method outperforms recent baselines by varying margins.\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths:\n1.\tThis paper is well written and easy to understand.\n2.\tThis paper investigates an important and interesting problem, i.e., open-set graph domain adaptation.\n3.\tThe model’s performance is pretty good compared with existing methods.\n# Weaknesses:\n1.\tThe authors fail to discuss and compare with a recent relevant baseline.\n2.\tAdditional ablation studies would strengthen the paper. For instance, it remains unclear how varying the number of known classes impacts the model’s performance.\n3.\tThe description of the training procedure is too long, which could be simplified.\n\nQUESTIONS:\n1. The authors should discuss and compare with the following AAAI 2025 paper [1].\n[1] Shen X, Chen Z, Pan S, et al. Open-Set Cross-Network Node Classification via Unknown-Excluded Adversarial Graph Domain Alignment, Proceedings of the AAAI Conference on Artificial Intelligence. 2025, 39(19): 20398-20408.\n2. Incorporating further ablation studies could enhance the paper. Specifically, the effect of varying the number of known classes—from 2 up to 4—on the model’s performance remains unexplored. Investigating this aspect would provide valuable insight into the model’s robustness under different complexities.\n3. The organization of the paper could be further improved by simplifying Section 3.5. Streamlining this section would enhance clarity and help readers follow the key concepts more easily, making the overall presentation more coherent and accessible.\n4. How does graph reprogramming affect the feature distribution of unknown classes? A detailed analysis of this effect would help clarify whether the approach effectively enhances the distinction between known and unknown classes.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-02"}