Title: One Prompt Fits All: Universal Graph Adaptation for Pretrained Models
Venue: NeurIPS 2025
Accepted: yes
Keywords: graph prompt learning

graph representation learning

few-shot learning
Abstract: Graph Prompt Learning (GPL) has emerged as a promising paradigm that bridges graph pretraining models and downstream scenarios, mitigating label dependency and the misalignment between upstream pretraining and downstream tasks. Although existing GPL studies explore various prompt strategies, their effectiveness and underlying principles remain unclear. We identify two critical limitations: (1) Lack of consensus on underlying mechanisms: Despite current GPLs have advanced the field, there is no consensus on how prompts interact with pretrained models, as different strategies intervene at varying spaces within the model, i.e., input-level, layer-wise, and representation-level prompts. (2) Limited scenario adaptability: Most methods fail to generalize across diverse downstream scenarios, especially under data distribution shifts (e.g., homophilic-to-heterophilic graphs). To address these issues, we theoretically analyze existing GPL approaches and reveal that representation-level prompts essentially function as fine-tuning a simple downstream classifier, proposing that graph prompt learning should focus on unleashing the capability of pretrained models, and the classifier should adapt to downstream scenarios. Based on our findings, we propose UniPrompt, a novel GPL method that adapts any pretrained models, unleashing the capability of pretrained models while preserving the input graph. Extensive experiments demonstrate that our method can effectively integrate with various pretrained models and achieve strong performance across in-domain and cross-domain scenarios.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a graph prompt learning (GPL) method, UniPrompt for unleashing the full pretrained graph models by leveraging input-level and layer-wise prompts while preserving the structure of the input graph. It identifies key limitations in existing GPL methods, such as a lack of consensus on underlying mechanisms and limited scenario adaptability. The authors conduct a theoretical analysis, showing that representation-level prompts are equivalent to fine-tuning a simple classifier, and argue that effective GPL should focus on enabling pretrained models to understand input data. Extensive experiments demonstrate the effectiveness of UniPrompt across multiple datasets in both in-domain and cross-domain scenarios.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1.The paper offers a perspective by emphasizing the importance of unleashing pretrained model capabilities rather than adapting them to downstream tasks.\n2.The authors evaluate UniPrompt on a wide variety of datasets, including homophilic and heterophilic graphs, and under different few-shot settings, providing robust empirical evidence.\n\nWeaknesses:\n1.While the perspective on representation-level prompts is interesting, the core methodology (e.g., kNN-based topology prompts, bootstrapping strategies) appears incremental and builds heavily on existing ideas in graph structure learning (GSL) and self-supervised learning.\n2.The theoretical results focus primarily on representation-level prompts but do not provide a rigorous analysis of input-level and layer-wise prompts, which are central to the proposed UniPrompt method.\n3.While the experiments are comprehensive, there is a lack of detailed ablation studies to isolate the contributions of individual components of UniPrompt, such as the kNN-based prompt initialization or the bootstrapping strategy.\n4.The paper highlights the strengths of UniPrompt but does not sufficiently discuss its limitations or scenarios where it may fail (e.g., datasets with extremely high heterophily or noisy features).\n\nQUESTIONS:\n1.How well would UniPrompt perform on real-world large-scale graphs, such as social networks or citation networks, where the kNN initialization and bootstrapping strategy might become computationally intensive?\n2.Why was kNN chosen as the basis for prompt graph initialization? Have other initialization strategies, such as random or learned topologies, been explored?\n3.The paper demonstrates that UniPrompt performs well across different pretrained models, but does the method rely on specific pretrained graph embeddings (e.g., DGI or GraphMAE), or is it agnostic to the pretraining approach?\n4.How sensitive is UniPrompt to the choice of hyperparameters like the temperature coefficient (τ) and the number of neighbors (k)? Could these parameters severely impact performance in certain scenarios?\n\nLIMITATIONS:\nYES", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work introduces a novel graph prompt tuning method, which is to address the lack of mechanism analysis and limited scenario adaptability. The authors theoretically prove that \"representation-level\" prompts are equivalent to linear probe, and they finds that prompt tuning should focus more on unleashing pre-trained model capabilities. Specifically, the method conducts the edge prompt using a kNN graph, and employs a bootstrapping strategy to integrate the prompt graph with the original graph. Extensive experiments demonstrate that the proposed method achieves good performance over classic prompt tuning/foundation models baselines.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The analysis of existing graph prompt work is valuable. This paper rethinks and categorizes existing methods, yielding valuable conclusions that should influence the graph prompt community.\n\n2. The theoretical analysis finds the equivalence between \"representation-level\" prompts and linear probe, and the authors propose that graph prompt tuning should focus more on unleashing the capabilities of pre-trained models. This perspective holds implications for the community of graph prompt tuning.\n\n3. The experiments are comprehensive, achieving excellent results across various scenarios, and include extensions to some graph prompt tuning baselines.\n\n4. The proposed method is simple yet powerful, and easy to follow.\n\nWeaknesses:\n\n1. Part of the Figure 1 is unclear. While I understand the authors' point, Figure 1a (input-level prompt) visually suggests adding a prompt to features, which doesn't fully capture the broader concept of \"modifying the input graph\" as described on Line 34. Figure 1 requires further improvement.\n\n2. I think the paper's introduction to layer-wise prompt methods is insufficient. While these methods may lack extensive exploration in the graph domain, they have shown significant influence in other fields, such as [J1, C1]. I suggest the authors provide additional analysis or discussion of these methods.\n\n3. The authors claim that \"only fine-tuning a classifier can achieve or exceed the performance of existing GPLs.\". However, this conclusion is only marginally demonstrated in the motivational experiments and isn't included in the main experiments. I think these linear probe results need to be fully added to the main table.\n\n[J1] Visual Prompt Tuning, ECCV-22\n[C1] MaPLe: Multi-modal Prompt Learning, CVPR-23\n\nQUESTIONS:\nThe authors claim that \"GPLs often experience performance instability or even negative optimization\", and Figure 2 illustrates unstable convergence for GPPT. While the paper does not delve deeply into the fundamental reasons behind this \"negative optimization\" or instability. I think the authors need more detailed analysis for several reasons. For instance, are we seeing severe overfitting in the few-shot setting, gradient issues (e.g., vanishing or exploding gradients)? And if so, how does uniprompt effectively mitigate these problems?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work focuses on the graph prompt learning problem (GPL), and proposes a GPL solution called UniPrompt. It first categorize the GPL solutions into 3 categories: (1) input-level, (2) layer-wise, and (3) representation-level, then point out the representation level GPL solution is equivalent to fine-tuning a downstream task classifier. Next, it proposes the UniPrompt which integrates a kNN-based structure to the original structure iteratively. Experiments cover in-domain/cross-domain, homophilic/heterophilic settings, and among all the proposed UniPrompt works well.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The proposed UniPrompt achieves strong empirical performance especially on some heterophilic datasets\n2. Comprehensive experiments are provided to examine the proposed approach under different settings\n3. Code is provided\n\n\nWeaknesses:\n1. The finding that representation-level prompt is equivalent to fine-tuning a task-specific classifier seems intuitive. According to figure 1, both are learning a function p_\\psi to the outcome of pretrained GNN. It seems less necessary to have a long redundant theorem to justify this.\n2. The paper discusses three prompting mechanisms (input-level, layer-wise, representation-level) as if they can be unified under a common framework (Equation 2), but it seems to me that the formulation only directly models input-level prompts and how the other two fit into this formulation remains unclear.\n3. While the theoretical analysis focuses extensively on representation-level prompts, the proposed UniPrompt seems to be an input-level method. It is unclear how the theory motivates or justifies the design choices in UniPrompt.\n\nQUESTIONS:\n1. Problem formulation for GPL - Equation (2) seems only suitable for input-level prompts? How would layer-wise and representation-level prompts fit into this framework? \n\n2. Relation between theory and approach - The core theoretical analysis (sec4) focuses on pointing out the representation-level solutions are equivalent to fine-tuning a task classifier. But the proposed approach (sec5) seems to be at the input-level. I wonder how does the theory inspire the final approach? \n\n3. Why some dataset has strong preference on UniPrompt - UniPrompt performs super well on Cornell, Texas, and Wisconsin. What characteristics of these datasets make UniPrompt particularly useful for them? Though the authors mention its due to heterophily, chameleon, actor, and squirrel are also heterophilic graphs but the performance gain is moderate. \n\n4. Sensitivity to tau - The performance appears sensitive to the value of $\\tau$ according to figure 4. And there is not single \\tau that could beat tau=1 (i.e. which diminish to the case that does not use UniPrompt) on all dataset. In that case, it brings hyper-parameter tuning workload when using UniPrompt. Is there any potential way to address this?\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper identifies an important limitation in current Graph Prompt Learning (GPL) methods—namely, the adaptation gap between pretraining and downstream tasks. The authors attribute this to unclear prompting mechanisms and poor scenario adaptability. They argue that current representation-level prompts act more like classifier tuning rather than leveraging pretrained models’ full potential.\n\nTo address this, the authors propose UniPrompt, a model-agnostic GPL framework that generates topology-aware prompts while preserving original graph structure. Extensive experiments on homophilic and heterophilic datasets under few-shot settings show consistent improvements over strong baselines.\n\nOverall, the paper offers a clear perspective shift for GPL and presents a well-designed method with solid empirical support. The contribution is relevant to few/zero-shot graph learning and Graph Foundation Models.\n\nSTRENGTHS AND WEAKNESSES:\nstrengths:\nWell-Identified Problem: The paper clearly articulates the adaptation gap in existing Graph Prompt Learning (GPL) methods, and substantiates it through both empirical observations and theoretical analysis.\n\nTheoretical Insight: The authors present a compelling theoretical result showing that representation-level prompts are essentially equivalent to classifier fine-tuning, providing a fresh lens to re-evaluate existing GPL paradigms.\n\nNovel and Practical Method: The proposed UniPrompt method is well-motivated and practically applicable across various pretrained GNNs. It leverages prompt-generated topologies without discarding the original graph structure, which improves adaptability.\n\nweaknesses:\n1.Graph prompt baseline could be added with one or two more.\n2.While the empirical coverage is broad, the discussion on limitations (e.g., computational overhead, scalability, or behavior on noisy graphs) is minimal and could be more thoroughly addressed.\n\nQUESTIONS:\nAs previously discussed.\n\nLIMITATIONS:\nAs previously discussed.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-03"}