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REVIEW 3 major objections 5 minor 165 references

Graph Prompting for Graph Learning Models: Recent Advances and Future Directions

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This survey claims that the scattered literature on graph prompting can be organized into a single three-part taxonomy—where the prompt acts on the input graph, on node representations, or on the downstream task—and uses that taxonomy to…

desk verdict A useful, up-to-date survey of graph prompting whose headline novelty claim is unsubstantiated because it never compares its taxonomy with the two earlier surveys it cites. read the letter →

arxiv 2506.08326 v1 pith:L7KLHGRE submitted 2025-06-10 cs.LG cs.AI

Xingbo Fu , Zehong Wang , Zihan Chen , Jiazheng Li , Yaochen Zhu , Zhenyu Lei , Cong Shen , Yanfang Ye
show 2 more authors
Chuxu Zhang Jundong Li
This is my paper
classification cs.LGcs.AI
keywords graphpromptingpre-trainingneuralnetworksprompttuningtaxonomyfew-shotlearningself-supervised
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This survey attempts to organize the fast-growing literature on graph prompting, the practice of adapting a pre-trained graph neural network to new tasks by learning small trainable prompt vectors while keeping the model itself frozen. It proposes that all existing graph prompting methods can be sorted into three categories according to where the prompt acts: on the input graph data, on node representations inside the model, or on the downstream task itself. If this taxonomy holds, it gives researchers a shared vocabulary for comparing methods that currently look unrelated, and a way to see which combinations of prompt levels have not yet been tried. The survey also catalogues the graph pre-training methods that prompts build on, surveys applications in recommendation, knowledge graphs, and biomedicine, and identifies missing theory, benchmarks, robustness, and LLM integration as open problems.

What carries the argument

The taxonomy itself is the carrying mechanism: the distinction between data-level prompting (transforming the input graph into a prompted graph), representation-level prompting (transforming the hidden or output representation matrix at a chosen layer), and task-level prompting (transforming the downstream task into a different form). Each category is tied to a concrete mathematical operation on a different stage of the pipeline, which is what makes the organizing scheme more than a list. The survey further splits data-level prompting into feature-based and insertion-based strategies, representation-level prompting into output and hidden variants, and task-level prompting into link-prediction-based and similarity-based variants.

What would settle it

A concrete refutation would be a graph prompting method whose prompt acts across all three levels in a way that cannot be represented by the taxonomy, or a documented demonstration that the dividing lines here match the taxonomy of the two earlier surveys the paper itself cites; either would show the claimed novelty or exhaustiveness does not hold.

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Extended reading notes

Core claim

The central organizational claim is that the entire space of graph prompting techniques can be captured by three categories defined by where the learnable prompt is inserted. Data-level prompting modifies the input graph, either by adding a prompt vector to node features or by inserting prompt nodes into the graph. Representation-level prompting multiplies output or hidden node representations by prompt vectors, using either a shared prompt or node-wise and layer-wise prompts. Task-level prompting reformulates the downstream problem, typically converting node classification into link prediction or into similarity matching against class prototypes. The paper presents this three-way split as a novel taxonomy that improves on earlier reviews, and uses it to structure a comprehensive summary of recent methods, their applications, and open challenges.

Load-bearing premise

The load-bearing premise is that the proposed three-category taxonomy is genuinely new and faithfully describes every method it places, because the paper's main contribution is organizational and would weaken if earlier surveys already draw the same dividing lines or if some methods are misdescribed.

Editorial extensions

If this is right

  • A researcher facing a new graph task can locate existing prompts by asking where the prompt acts in the pipeline, making method selection a mapping task rather than a literature search.
  • Because data-level prompting requires a full forward and backward pass while task-level prompting does not, the taxonomy exposes a cost and compatibility trade-off that future prompt designs can optimize.
  • The taxonomy suggests that prompts at different levels are complementary, so combinations such as feature-based prompts with similarity-based task reformulation are a natural next direction.
  • Task-level prompting is currently limited to classification; the survey's framing implies that regression and other task types still lack a principled prompting formulation.
  • The review redirects the field's open challenges to benchmark standardization, theoretical foundations, universal compatibility, robustness, and LLM incorporation.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the taxonomy is right, the prompt level and the pre-training task form an implicit design matrix; many cells of that matrix, such as hidden-representation prompts applied to a multi-task pre-trained model, remain untested, and filling them would be a cheap empirical programme.
  • The data-level prompt update rule resembles an adversarial perturbation under a norm constraint, which suggests that the adversarial robustness questions the survey raises could be studied with existing tools from adversarial example analysis, an extension the paper does not make.
  • A testable prediction follows from the taxonomy: methods in the same category should show similar failure modes and transfer behaviour across datasets, whereas methods in different categories should fail differently, and a meta-analysis of published results could check this without new experiments.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. This survey reviews graph prompting, the approach of adapting pre-trained graph learning models to downstream tasks by learning trainable prompts while keeping the model frozen. It first summarizes graph pre-training foundations (generative, contrastive, and multi-task methods), then proposes a taxonomy of graph prompting techniques into data-level, representation-level, and task-level prompting. It also surveys applications in recommendation, knowledge engineering, biology, and medicine, and closes with open challenges and future directions. The paper's stated contribution is organizational: a novel taxonomy and an up-to-date compilation of recent methods.

Significance. If accurate, the survey would be a useful and timely reference for researchers entering graph prompting, and its three-level taxonomy (data, representation, task) is a plausible organizing scheme. The paper is broad and current, covering both foundational pre-training methods and very recent prompting techniques, and it includes a helpful summary table. Because the contribution is organizational, the accuracy of method descriptions and the distinctiveness of the taxonomy are load-bearing; the current inconsistencies and the unsubstantiated novelty claim diminish but do not destroy the survey's value. The paper contains no derivations, so there is no circularity concern; self-citations are used as ordinary references to relevant prior work.

major comments (3)
  1. [Section 1, first contribution bullet] The novelty claim for the taxonomy is not substantiated. The text says the data/representation/task split is proposed 'Compared to previous survey papers [61,87]', but the paper never describes the taxonomies in either survey, never provides a comparison table, and never states a distinguishing criterion. Since this is the paper's primary stated contribution, the authors should either provide a side-by-side comparison showing what the new taxonomy adds, or restate the contribution as an up-to-date survey organized by a convenient three-way split. This is a concrete, checkable claim about two cited documents, not a matter of taste.
  2. [Section 4.1.1, Eq. (6)] The description of GPF-plus is internally inconsistent. The text first says GPF-plus 'assigns an independent prompt vector to each node', then says it uses attentive aggregation of basis vectors, and Eq. (6) defines each prompt as a weighted combination of shared basis vectors. An independent per-node vector and a shared basis set with per-node weights are different mechanisms. Please clarify which mechanism is being described and reconcile the surrounding prose with Eq. (6); as written, a reader cannot tell what GPF-plus actually does.
  3. [Table 1] Several methods appear in more than one category without explanation. GraphPrompt appears under both Output representation prompting and Similarity-based prompting; GraphPrompt+, HetGPT, and ProNoG similarly span representation-level and task-level rows. Section 4 introduces the three categories as a division of techniques, which implies they are mutually exclusive at the method level. If a method legitimately operates at multiple levels, the paper should say so explicitly and mark primary versus secondary categorizations; otherwise the taxonomy's consistency is undermined.
minor comments (5)
  1. [References] The reference list contains duplicate entries for the same works: [9] and [10] are both the GPF paper, [19] and [20] are both GraphSAGE, [118] and [119] are both GraphCL, [124] and [125] are the same non-homophilic graph paper in preprint and published form, and [138] and [139] are both the CoOp paper. These should be merged.
  2. [Table 1] The abbreviation 'DT Universality' is not defined anywhere; please spell it out in the caption or in the text.
  3. [Section 4.3.2] The sentence 'Therefore, no additional prompts are introduced in similarity-based prompting' conflicts with the immediately following sentence stating that some studies design class prototypes as learnable vectors. Please rephrase to distinguish prototype-averaging methods from learnable-prototype methods.
  4. [Section 2.1] The phrase 'an attribute graph' should be 'an attributed graph'.
  5. [Figure 2] The taxonomy diagram is dense and would benefit from a legend or from matching color/shading to the three prompting levels, since the table and text use the same categories.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is a survey that reviews existing graph prompting techniques rather than deriving new results; self-citations are bibliographic references and the unsupported novelty claim is a factual claim about prior surveys, not a definitional reduction.

full rationale

The paper's content is a literature review organized into pre-training methods, prompting techniques, applications, and future directions. It does not derive new theoretical results, fit parameters to data, or present a prediction that is equivalent to its inputs by construction. The central contribution is a proposed taxonomy of graph prompting methods into data-level, representation-level, and task-level prompting. The paper asserts this taxonomy is novel compared to prior surveys [61, 87], but it does not provide a side-by-side comparison. Even if this novelty claim is unsubstantiated, that is a concern about novelty or scholarly accuracy, not circularity: the taxonomy's internal organization does not depend on any equation that is defined in terms of the conclusion, nor does it rename a fitted quantity as a prediction. The self-citations present in the paper (e.g., EdgePrompt+ [12], IAGPL [51], the graph foundation models survey [105], and a recommendation systems paper [145]) are used as references to the authors' own relevant prior works, which is normal scholarly practice; none of these citations is invoked as a load-bearing premise to justify the claimed taxonomy or to forbid alternative organizations. Section 6 candidly identifies limitations, including the lack of standardized benchmarks, underdeveloped theoretical foundations, and underexplored robustness, and these statements do not smuggle in conclusions. Because the survey is a review rather than a derivation, no circular step can be exhibited from the paper's own equations or citation chain. The appropriate finding is therefore no significant circularity.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The survey's central value is its organizational scheme and summaries of existing work. It relies on the accuracy of its descriptions and the novelty of its taxonomy, both of which are assumptions that could be challenged. No free parameters or invented entities are introduced.

assumptions (3)
  • domain assumption The proposed three-category taxonomy (data-level, representation-level, task-level) provides an exhaustive and disjoint partition of graph prompting methods.
    The entire organization of Section 4 rests on this; if a method belongs to multiple categories (e.g., GraphPrompt appearing in both output representation and similarity-based categories in Table 1), the disjointness claim is weakened.
  • domain assumption Graph prompting methods are accurately described by the summaries in this paper.
    The survey's value depends on correct representation of the cited works; individual descriptions may contain errors (e.g., the GPF-plus description in Section 4.1.1).
  • domain assumption The cited prior surveys [61, 87] do not already contain the same taxonomy.
    The paper claims novelty over prior surveys; if they already use a similar data/representation/task categorization, the novelty claim fails. We cannot verify this without reading those surveys.

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Cite this review

Pith. "Pith review of Graph Prompting for Graph Learning Models: Recent Advances and Future Directions." pith.science (2026). https://pith.science/paper/L7KLHGRE

@misc{pith2026250608326,
  author       = {Pith},
  title        = {Pith review of: Graph Prompting for Graph Learning Models: Recent Advances and Future Directions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/L7KLHGRE}},
  note         = {Machine review of arXiv:2506.08326}
}
read the original abstract

Graph learning models have demonstrated great prowess in learning expressive representations from large-scale graph data in a wide variety of real-world scenarios. As a prevalent strategy for training powerful graph learning models, the "pre-training, adaptation" scheme first pre-trains graph learning models on unlabeled graph data in a self-supervised manner and then adapts them to specific downstream tasks. During the adaptation phase, graph prompting emerges as a promising approach that learns trainable prompts while keeping the pre-trained graph learning models unchanged. In this paper, we present a systematic review of recent advancements in graph prompting. First, we introduce representative graph pre-training methods that serve as the foundation step of graph prompting. Next, we review mainstream techniques in graph prompting and elaborate on how they design learnable prompts for graph prompting. Furthermore, we summarize the real-world applications of graph prompting from different domains. Finally, we discuss several open challenges in existing studies with promising future directions in this field.

Figures

Figures reproduced from arXiv: 2506.08326 by the authors.

Figure 1
Figure 1. The workflow of the "pre-training, prompting" [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Taxonomy of techniques in the "pre-training, prompting" scheme. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

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