One Prompt Fits All: Universal Graph Adaptation for Pretrained Models
Authors: Yongqi Huang
Jitao Zhao
Dongxiao He
Xiaobao Wang
Yawen Li
Yuxiao Huang
Di Jin
Zhiyong Feng
Venue: NeurIPS 2025 poster ()
Keywords: graph prompt learning
graph representation learning
few-shot learning
Primary area: general_machine_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.

One Prompt Fits All: Universal Graph Adaptation for Pretrained Models
Yongqi Huang': Jitao Zhao' Dongxiao He': Xiaobao Wang', Yawen Li²,
Yuxiao Huang', Di Jin', Zhiyong Feng'
Coolege of Intelligence and Computing, Tianjin University, School of Economics and Management, Beijing University of Posts and Telecommunications,
DDepartment of Data Science, George Washington University, 1 Kyqhuang, zjtao, hedongxiao, wangxiaobao, jindi, zyfeng)@tju edu cn wwrmly07760126. com, "yuxiaohuang@gwu.co
edu
Abstract
Graph Prompt Learning (GPL) has emerged as promising paradigm that bridges graph pretraining models and downstream scenarios, mitigating label dependency and the misalignment between upstream pretraining and downstream tasks. Al- though existing GPL studies explore various prompt strategies, their effectiveness and underlying principles remain unclear. We identify two critical limitations: (1) Lack of consensus underlying mechanisms: Despite current GPLs have advanced the field, there is no consensus on how prompts interact with pretrained models, different strategies intervene varying spaces within the model, i.e., input-level, layer-wise, and representation-leve prompts. (2) Limited scenario adaptability: Most methods fail generalize across diverse downstream sce- narios, especially under data distribution shifts (e.g.. graphs). To address these issues, we theoretically analyze existing GPL approaches and reveal that representation-leve prompts essentially function as fine-tuning simple downstream classifier, proposing that graph prompt learning should fo- cus 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 demon- strate that our method can effectively integrate with various pretrained models and achieve strong performance across in-domain and cross-domain scenarios.
Introduction
Graph Prompt Learning (GPL) 112, which aims to design diverse graph prompt strategies, has emerged as promising and effective alternative paradigm that bridges between graph pretrain- ing [3] and downstream scenarios (5), overcoming the limitations of label dependency and the misalignment between upstream pretraining and downstream tasks (6]. Most GPLs freeze the pa- rameters of the pretrained model and tune specific prompt module. Due to its compatibility with various types of graphs, such as general graphs (1. Knowledge Graphs (KGs) [8] 9 TO) and Text- Attribute Graphs (TAGs) [1]] GPL demonstrates strong universality and transferability. thereby improving the fields of few/zero-shot graph learning [12 13), unified task learning [14 [3] [6]. cross-domain graph learning [17 (8), and promoting the development of Graph Foundation Models
(GFMs) 19 20 21)
'Equal contribution. "Coresponning author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Prompt (g
Pretrained Model +
Pretrained Model
*
Pretrained Model #
H
Prompt
P+
fum
H
H
Pv (H)
Prompt
96
ge
90
(0)
loss
(b)
loss
loss
Figure 1: Three different graph prompting mechanisms: input-level prompt (left), layer-wise prompt (middle), and representation-levll prompt (right).
Most GPLss can be divided into three categories according to how the prompts are integrated into the pretrained models, as illustrated in Figure [ Input-level GPLs (Figure [T] like feature prompt 77 and edge prompt [22 23 insert prompt modules or soft prompt before the pretrained models, effectively modifying the input graph align with upstream distributions in pretraining. Representation-level prompts (Figure [ including task tokens [6] and prototypical subgraphs [24] 25 applies prompts to the representations generated by the pretrained models, formulating downstream tasks that align with the pretrain objectives. Layer-wise prompt (Figure [p) [15] 266 combines the prompt with each layer inside the pretrained model, learning the distribution and propagation patterns in each layer. In addition, some works [26, 27] also explore hybrid strategies that place various prompts across different layers or components (input-, representation-levell of the model.
Despite the success of these explorations, our observations indicate that GPLs often experience performance instability or even negative optimization, which also be reported in recent studies (2). Moreover, the existing prompt methods are complex and diverse. There remains a lack of clear understanding of why graph prompt learning works. Through our analysis of existing methods, we identify two major issues: 1). Lack of consensus on underlying mechanisms: Although various graph prompt strategies have advanced the field, there remains rare unified understanding of how these prompts interact with pretrained models. As shown in Figure [ diverse mechanisms such as and layer-wise prompts achieve promising performance. However, they influence the models in different ways, and the underlying interaction mechanisms are still unclear. 2). Limited scenario adaptability: Most GPLs struggle to achieve good performance on different pretrained models even in the in-domain setting. As shown in Figure 2 only fine-tuning a classifier can achieve or exceed the performance of existing GPLs. In addition, these methods have difficulty achieving excellent performance in a variety of downstream scenarios, especially when the data domains of upstream and downstream scenarios are different (e.g., from a homophilic pretraining graph to a heterophilic downstream graph). To summarize: From the prompting mechanism to the downstream scenario, existing graph prompt learning methods exhibit an adaptation gap.
input-level, representation-Levee
To investigate the underlying mechanisms of GPL, we conduct a motivation experiment and find that existing representation-leve prompt GPLs fail to consistently adapt well different pretrained models. Moreover, they show no significant performance improvement compared to linear probe (only fine-tune a classifier), which achieves good and stable results. This motivates us to explore the relationship between different types of prompts and linear probe. Through theoretical analysis and discussions, we demonstrate that the representation-lest probe. This primarily serves to adapt the pretrained model to downstream tasks, which focuses on fitting the outputs of the pretrained models to the downstream labels, struggling to leverage the unique benefits of prompts. As for layer-wise methods, their reliance on layer-wise representations of the pretrained model, combined with their design complexity, makes them unsuitable. In contrast, input- level prompts avoid the limitations and preserve the advantages of prompting, they are the promising among the three categories. Therefore, we propose a perspective: graph prompt learning should focus on unleashing the capability of pretrained models, and the classifier adapts to downstream scenarios.
prompt is essentially equivalent to linear
Based on our perspective, we propose UniPrompt, a novel GPL method that adapts any pretrained models, leveraging prompt graph while preserving the original structure to unleash the capability of pretrained models. Specifically, we construct a kNN graph as the initial prompt graph and adaptively optimize edge weights to guide message passing across nodes. To preserve the input graph, we introduce a bootstrapping strategy that integrates the prompt graph into the original graph topology. preventing model collapse and overfitting. Our main contributions can be summarized as follows:

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1. We identify two key issues in existing GPLs: lack of consensus underlying mechanisms, and limited scenario adaptability. We propose that graph prompt learning should focus on unleashing the capability of pretrained models, and the classifier adapts to downstream scenarios.
2. We propose UniPrompt, novel universal GPL. method that adapts any pretrained models. This method leverages a learnable prompt graph while preserving the original structure to unleash the capability of pretrained models.
We conduct extensive experiments on homophilic and heterophilic datasets, evaluating in-domain and doms-- performance under few-shot settings. Experimental results demonstrate that our method consistently outperforms state-of-the-art GPL baselines.
Notations and Preliminary
General Graphs. Given graph G (V,E, where V {e1. 02,-.. ,UN is the set of nodes, N VI. and & C V x the set of edges. These nodes are associated with feature matrix X E R NXF X E R is the feature of ot. The edges be represented by adjacency matrix A E (0. [0,1)NxN and Ai = iff Uy) E E. Each node et is associated with a label yi E Y, where Y denotes the set of all possible class labels. We use P(-) denote the probability distribution, primarily for distinguishing concepts rather than performing mathematical derivation.
Fine-Tunme Tuning. In the weerainnfiinttur paradigm, given pretrained graph encoder fe and a down- stream trainable projection head Jø+ both parameters and e are jointly optimized on a downstream dataset D {A, X, Y)J. The objective is to maximize the log-likelihood of label predictions, which can be formulated as:
N Σ log P (Vi Io (fo(A,
max 0,0
ID]
(A.X,Y)ED
where fo(A, X), denotes the node representation of the node e; As special case, when freezing 0 (i.c., restricting max&). this reduces to linear probing where only the projection head Jo is adapted.
Graph Prompt Learning. Compared to fine-tuning, graph prompt learning keeps the pretrained encoder fo frozen while introducing trainable prompt parameters The optimization objective for all graph prompt learning methods can be expressed as:
N
max V
Σ (A.X,))
Σ
log P (y: Predicty (A, us; (A; So))
(2)
D
where D ((AA Y)} is downstream dataset, v represents all trainable prompt parameters, fo the frozen pretrained encoder. Predicty (-) is a unified prediction function that takes the input graph X), node u, the pretrained encoder fe. to predict the label of node 0;.
For input-level prompt, V acts on the input (A, X), transforming it before enters fo. For layer-wise prompt, V is integrated within the layers of So. For representation-level prompt, V operates on the representations from fe. often as part the classifier, directly influencing the downstream tasks.
Motivation Experiments and Analysis
Although graph prompt learning is theoretically distinguished from fine-tuning by freezing the parameters of the pretrained model to retain pretrained knowledge, this seemingly suggests clear boundary between "unleashing pretrained knowledge" and "adapting to downstream scenarios". which contradicts the problem raised in our Introduction. However, we refute this claim through an experiment, demonstrating that prompt-based methods fall into a "pseudo-adaptation" trap.
To investigate whether graph prompt learning suffers from adaptation bias, we conduct the fol- lowing experiment. As illustrated in the Figure 2 we select classic GPL methods, CPPT [ and GraphPrompt [24), and compare them with fine-tuning that only optimizes the classifier. We employ widely used pretrained models: DGI (28] GRACE [29]. and GraphMAE (30), and we keep all other parameters consistent, and observe the differences in downstream training between prompt learning and fine-tuning, Our experimental results reveal that GPPT exhibits significant incompatibility when

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DGi Pretrained
GRACE Pretrained
GraphMAE Pretrained
Rie-tunelC), Acc: 49.93
1.00
Fine-tune(Cl, Acc: 50.66
Fne-tune(C), Acc: 50 03
GPPT, Acc: 37.59
GPPT, Acc: 42.19
1.00
GPPT, Acc: 41.80
Loss (Normed)
GraphPromps,
0.75
GraphPrompt.
0.75
Graphiromps, 51.45
0.50
0.50
0.25
0.25
0.00
0.00
50
100
150
200
20
40
20 Epech
Epoch
Epoch
Figure 2: Comparison of fitted normalized loss curves and 1-shot performance across three pretrained models on the Cora dataset. Fine-tune(C) denotes linear probe, while GPPT and GraphPromi are two GPL methods. Shading indicates the standard deviation of the sliding window.
switching pretrained models, whereas GraphPrompt maintains stable performance across different pretrained models. Another observation is that even though GraphPrompt shows good convergence trends, its performance still falls short of the simple fine-tuning approach in many scenarios, which remains robust across different pretrained models and even outperforms GPL methods in some cases.
This suggests that different graph prompt learning methods heavily depend on the design of the pretrained models. When confronted with varying pretrained models, they often demonstrate incom- patibility or suboptimal performance compared to fine-tuning. This raises an important question: Do existing graph prompt learning methods work due to the design of the prompt algorithm, or are they merely benefiting from certain key components in the pretrained model? The answer remains unclear. However, our observations align with prior research [2], when pretrained models are swapped, many GPL methods underperform, while simple fine-tuning can achieve superior results.
Thus, we challenge the current objectives of GPLs: Have existing studies truly succeeded in dis- tinguishing graph prompt learning from fine-tuning? To get deeper insight into the performance differences across various prompt spaces, we focus on a key question: do these differences stem from the prompts' ability to access pretrained knowledge, or from their capacity to adapt to specific tasks? Therefore, we provide a theoretical analysis in this section understand the underlying mechanisms.
4 Mechanism Relationship between Prompts and Classifier
Definition 4.1 Given a GNN encoder ((:;)) V - Rd, the representation set H = (hr E Rd v V} is encoded by c. The representation of v; is h2 = (((v;)). Then, we define the prompt function T() and classifier C() and baseline classifier Co(-) as follows:
T(.) IRd + Rdd, C(.): R Col.) Rdd RR
where T()) is parameterized as linear transformation: T(h) = W, + br, with parameter WT E Idd'x xd, by E Rdd, C(-) is implemented
linear classifier: C(h) W with
Wc E gd' xk, Col-) is implemented as a baseline classifier: Co(h) W h. with Wo Baxk
Theorem 4.1 (Parameter Objective Equivalence) Given a linear prompt function T(h) = Wyh + br and classifier C(h) Wh,,, the following properties hold:
1. Function Space Equivalence: There exists a linear classifier ("(h) WT h bo such that (C o Th) = (h) for all h;
2. Optimization Objective Equivalence:
The optimization problems minWr.br.W. br.WWe L(C
Thh). y) and minw bey (b),)) are equivalent in parameter space and gradient update paths.
The function space equivalence is guaranteed by Proposition 4.1 and the optimization equivalence is demonstrated in Proposition
Proposition 4.1 (Function Space Equivalence) For any linear transformation T(h) = Wrh+hb and classifier C(h) = WTh, there exists an equivalent classifier C'(h) = WT h + bo such that o (h).

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The detailed proof of Proposition 4.1 is provided in Appendix A.1 It shows that a representation-lvvel prompt is functionally equivalent to linear probe C' in the tunction space. While this equivalence has not been explicitly recognized in prior work. To further clarify the equivalence between representation-leve prompt and linear probe in terms of optimization, we introduce Proposition 4.2
Proposition 4.2 (Optimization Objective Equivalence) For (C ()))) and C" C"(h), we consider the same loss function I, the optimization problems minw,, Wo.br L((C a ()))) and
minw...er (b), y) are equivalent in the parameter space.
The detailed proof of Proposition 4.2 is provided in Appendix A.2 Here we give a brief explanation: Proposition 4.2 demonstrates that the two different optimization formulations, representation-leve prompt and linear probe, lead to equivalent parameter updates during optimization. This theoretical equivalence implies that both approaches perform the same underlying optimization. While practical performance differences arise due to the structural complexity of prompts, which may introduce extra challenges, as opposed to linear probe that is typically simpler in design and optimization.
Discussion 1. Theorem 4.1 demonstrates that the representation-level prompt is fundamentally equivalent to linear probe, only designing a simple classifier can yield satisfactory results. Therefore, we suggest that graph prompt learning should focus unleashing the capability of pretrained models, rather than adapting pretrained models to specific downstream scenarios. Specifically, an effective GPL approach should aim to combine the advantages of both mechanisms. Let pre (: 9) AX pre be a pretrained GNN encoder with fixed parameters W pre- We define å -= (A, x) T()), which modifies the input graph to align the Ppre- C(h) = Wih adapts representations to downstream labels. Then, the joint optimization of (9, Wc) as follows:
min LD = We.
Σ yi log (WJAXW 0.EV2
where L. is the downstream task loss, typically cross-entropy loss. AXW, pre ensures adaptation with pretrained knowledge, and We minimizes Lp to adapt downstream task.
Discussion 2. Building Discussion 1, propose that the input-level and layer-wise prompt mechanisms align with equation [ where jointly optimizing the prompts and the classifier leads to better performance than using the classifier prompt only. As for layer-wise methods, their reliance on layer-wise wise representations of the pretrained model, combined with their design complexity, makes them unsuitable. In contrast, input-level prompts avoid the limitations and preserve the advantages of prompting, which applies prompts to the features or adjacency matrix, helping to reduce structural differences and feature distribution shifts, thereby bridging the gap between upstream pretraining and downstream scenarios. Therefore, we propose that graph prompt learning should focus on unleashing the capability of pretrained models, and the classifier adapts to downstream scenarios. This viewpoint clearly defines the distinct roles and mechanisms of the two crucial downstream components: the prompt and the classifier.
5 Methodology
In this section, we present our method, UniPrompt. Our approach introduces an input-level graph prompt that modifies the graph topology to better align the pretrained models with downstream few-shot tasks. We first introduce an overview of our UniPrompt. For given graph G = (A, X) and a frozen pretrained model fo(-), our goal is to adapt it to a downstream task with only a few labeled nodes Vz. Instead of directly fine-tuning 0, UniPrompt generates a topological prompt Á with learnable parameters V This prompt is then iteratively fused with the original graph create a prompt graph, which is fed into the frozen encoder (-). Finally, a lightweight classificr (.) is trained jointly with the prompt parameters V the labeled nodes.
Prompt Initialization. To enhance prompt adaptability for pretrained models, we consider both in-domain and cross-domain scenarios. In the in-domain case where pretraining and prompt tuning share the same data distribution, the classifier adapts the pretrained model to downstream scenarios while the prompt aligns the pretrained model with downstream inputs. A special case occurs when downstream data is heterophilic, even with matched distributions, the heterophily contradicts the homophily assumption in pretrained models. Existing input-level and layer-wise prompts primarily
5

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process features and tend to overfit in few-shot settings, failing to handle this scenario. In contrast, topological relationships provide more direct and interpretable structural patterns. Therefore, we propose an edge prompt strategy that uses NNN to generate topological prompt with tunable edge weights, formulated as:
Siij if Siy top-k (S (Ainit 0, otherwise.
X;X
Sty
(5)
Xx|
where X;, Xj E IR are the features for nodes u, and Vj. and || |l2 denotes the L2 norm. we select the top-k edges initial edges and serve the basis for learnable prompt.
Parameterization. Instead of treating the presence of edges as fixed, we introduce learnable parameters to control the importance of each edge in the initial prompt graph. For every non-zero edge (Aii))) ).j associate learnable scalar weight Way which forms our of prompt parameters = }- )- To enable the model to select the most relevant prompt edges and ensure non-negative weights, apply gating mechanism using scaled and shifted ELU activation function. The prompt adjacency matrix A is computed as:
Ào - ELU( a a) +1,
(6)
where a is a hyperparameter controlling the shape of activation. This parameterization adds Iearn- able edge gating mechanism that can adaptively prune (i.e., approach zero) or amplify topological information for downstream scenarios.
Bootstrapped Prompt Integration. After generating the prompt topology A, the challenge lies in its integration with the original adjacency matrix A. While an ideal scenario would involve directly substituting A with A, this approach proves impractical, particularly in few-shot learning settings, due to risks of severe overfitting and model collapse. Drawing inspiration from Graph Self-Supervised Learning (GSSL) [31 32 and Graph Structure Learning (GSL) [33,, we adopt bootstrapped integration that iteratively updates the topology rather than directly replacing A. The graph structure fed into the pretrained model at each training epoch is iteratively updated. Let Á (() be the input adjacency matrix training epoch The update rule is defined as:
(4) 7 (t-1) + (1 - A)
where (0) A is the original adjacency matrix, and the temperature coefficient r E [0, 1] controls the balance between original and prompt topology.
Optimization Objective. For subsequent epochs, the input to the pretrained model becomes 9 (A, X). Through UniPrompt, we process & via the pretrained model to obtain node representations H. Our empirical results in Figure 2 demonstrate that linear probe achieves comparable performance to existing GPL methods in few-shot settings. This demonstrates the capability of the classifier to adapt to downstream scenario, confirming its effectiveness in this configuration. Therefore, we incorporate a learnable projection head 9s in the representation and jointly optimize with UniPrompt. The overall framework is optimized via the following equation:
min
Σ lD (as (Jo (po X)).) WEV.
(8)
where yi is the ground-truth label of node vs E VL, and ln is the downstream task loss, i.e., the cross-entropy loss for classification tasks.
Experiments
6.1
Experimental Setup
We evaluate the effectiveness of UniPrompt "using nine node classification datasets, including three homophilic datasets Cora [34] SiteSeer
and PubMed [34, and six heterophilic datasets
Cornell [5). Texas (35]. Wisconsin 35 Chameleon
35 Actor
35 and Squirrel [35]. For in-domain
'Code is available at: http://giiibbbommoooooooooooooou

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Table 1: In-domain node classification. Accuracy on 1-shot node classification tasks over three pretrained models and nine datasets. The best results in each pretrain strategy are highlighted in bold, and the runner-up with an underline.
Pretrain
Methods
Cora
CleeSeer
PubMted
Corneil
Texas
Wisconsin
Chameioon
Actor
Squirrel
Fine-Cune
50.22
42 28
G8.00
Hi $..4
37.
88,91 10.55
24.42
21:38 5.25
22 22.27
Linear-prabe
40. Fr
43. 10
55, 76
34.56
56 21
38.71 18.38
25 25:64.22
21 21.35,2.12
22.50
GFPT
36, 26.01
51. 61.06.6.34
29.01
31.
28,
22
19.81;1.51
23.7
GruphPrumph.
82. 20
43 61
22. 22.62
25
19.84,5.79
22.65
DGT
All
28. 17
25.87
55
26.07
51
33.
24. 24,82
22 21.4
20.31:4:7
44 24.
12.58
25.177
22.58.1.06
20.51:3.c5
21.56
PropPr
47.07
23:
21,38 1:77
21.000
PiguPr
74 To
10
26.73
28.37
20
22.55 76
20.16
21.74_22.
UnoPpo
40. 15.50g as
45.57
57.17 c7 11
51.13
48.2 1.
25.73
25.34 09
24.20 15
Pine tune
45. 3.50gamm
46 16
57.97 27.00
34,18 20.1a
31. 51.50
32 25
26 22 u2 c9
20.81
00 20.00
Lineaz- probe
46. 2..25 s0
46. lllass
67.87
17:00
15.2
54. 14.84,15
31 M.L.
24 12 04
21.55
20.81 L.
GOPT
42. 1919 cs
37 12
47.02g3
27. 58.5.0
$2: 27.07
26 55
25. 16
19.50
21 11.56.m
GraphPrruph.
15.9
55 24
63-6329.11
23. 23
90 13
23
28.28 24.3
19.15
22. 22.48.00
GRACE
CFF
All in one
18. 17
51.50
21 2100
36, 1400
36.51a7.4
28, 23:3:33
22.
27:31
25 a44
27. 88.774
36. 17
20 20:26
19 16
2..2
19.04,4.33
20 20.50
22 00
GPPs
47 47.06ga
44 46
61.58.1-1
28,
51 20
27 27..00....
26 23
20. 15
21.41
RigeProopp:
41 11.86
56 20
18.20,31.84
31. 85...33
29:
38 38.02.8.25
25
20.75,2.77
21.76
BagePre
45. 45.32
SS 80
50.01:11:99
12.15
S1. 96.
38
25, 25.79
20.65:2.0
20.00
CalPromps
44.70amm
48.58
67.55.4.59
62.50).1..0
45.38110.77
50.96
26,23
1..3
Pine-Lume
36.
64.29.0.32
Hr.82 11.30
37.
34 G4 10:33
22
3.0h
2.60
Linear-probe
12:3 c
08
58.61
27 14
58 01
28.
m
20.56..111
CPPT
0..10...28
26.74
35, 16:15:12
25.88
19,36
20.50
OraphProupt
37. 77 41.45
50.87.6.:44
23.5222..5
13.04.11 me
26. 28
22.05.2..0
17,82,2.44
g3.7
GragiMAR
GPV
All-in-tmw
46. 7454 xo
40 1000saas
31. 72
55 15.55
39.99.4.31
27.2 11
23.35
29. 71 10
28 28 on
25 15
22 22.30=22.0 56
00.38:1.0 a1
21,08,2.7
20.20,,.14
20.30
10 a0
GPP4
43.30 a3
40 15
52.9221..5
26. 15.45
34 31.83.10 ds
28. 79as.44
22. 22:35_...9
20, 20.64.3.54
20.20
Exgo
30:162ase
33 00
40.79.1.47
25.26.1.23
33, 02.0011mm en
26.022s.30
22:27 22:27
19.93
20. 20.16:1.0
uxplit
40:11ammis
57 13
60.77.1.31
26.15
54 24.21a40
26, 26.84
22. 22.47_3.00
20.20,,..0
20.78
GaiProaps.
47
49.29
07.47
21.28
49 49.88
61.38_2110
24.29g3.4
28.35
22.08
settings, we use DGI 28 GRACE 29]. and GraphMAE 30 as pretrained models, and we compare
our method with two baseline tuning methods, and seven classic and state-of-the-ie GPL. methods,
including Fine-tune,
Linear-probe (fine-tune classifier only), GPPT (6) GraphPrompt (24).
All-in-one [14], GPF 77. GPF-plus 77]. EdgePrompt [23]. and EdgePrompt-plun [23]. For
cross-domain settings, we adopt FUG (36) as the pretrained model, and we compare our method with
four types of baseline methods, including: (1) Semi-Supervised baselines: GCN [3) GAT (4. (2)
Graph Self-Super Learning baselines: DGI (28), GraphCL [37]. (3) Graph Prompt Learning baselines: GPPT (6) GPF (7). (4) Multi-domain Graph Pre-train baselines: GCOPE (38), MDGPT [39],
MDGFM (27). In our experiments. To ensure performance reliability, we perform 20 repeated runs for each of 5 fixed random seeds, reporting averaged results over 100 trials. Detailed information about
the experimental setup can be found in the Appendix B.T
6.2 In-Domain Node Classification
1-shot node classification on different pretrained models. We report 1-shot node classification on nine datasets using three pretrained models. As shown in Table [ our method outperforms existing GPLs across most datasets under different pretrained models. Specifically, we observe the most sigmificant improvements on the Cornell, Texas, and Wisconsin datasets, where our method surpasses all existing GPLs. This is primarily because these GPL baselines struggle to adapt downstream datasets to the pretrained model, particularly for heterophilic graphs which pose a significant challenge. In larger heterophilic datasets like Actor and Squirrel, the dense connectivity and size of these datasets make baselines challenging in the 1-shot setting. Existing methods are unable to leverage representation-les level prompts or directly process features or edges to improve performance. As result, these methods suffer from model collapse overfitting. For homophilic datasets, such as Cora and CiteSeer, all basclines perform well, resulting in limited improvement for our approach. However, for PubMed, which has fewer classes, our prompt graph introduces additional homophilic edges, providing an advantage over other GPL baselines. Moreover, we observe that the choice of pretrained model has an impact on downstream prompt tuning. For example, on the Chameleon dataset, under the DGI and GRACE pretrained settings, all baselines perform comparably to or even better than our method. However, when switching to GraphMAE, the performance of all methods drops sharply. Similar trends are observed on CiteSeer and PubMed, where our model demonstrates greater stability compared to other baselines.
1/3/5-shot Node Classification Performance on DGI. To further demonstrate the adaptability of method, we conduct 3-shot and 5-shot experiments GPL baselines using the DGI-pretrained
7

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Table 2: Cross-doman node classification. Accuracy on 1-shot node classification tasks over six datasets. Each column represents test domain, while others are train domains. The best results are highlighted in bold, and the runner-up with an underline. Methods with are reported from 227
Methods
Cora
Citeseer
PubMed
Cornell
Squirrel
Chameleon
GCN
28.57
31.27
40.55
31.81
20.00
24.17 #5.21
GAT
28.40_6.25
30.76 40
39.9914.96
28.03
21.55+2.30
23.93++411
DGI
29.30_t.82
30.03
41.8547.78
31.54415.66
21.15+1..8
21.73
GraphCL*
34.94_6.40
30.58 4.58
40.37
27.15412.64
21.424:2.23
22.49
GPPT#
17.52_5.52
21. 21.45
36.5645.a1
25.09
20.09
24.53
GPF*
37.84_11.07
37 #8.87
46.3647.48
34.5447.73
21.92+33.0
25.90
GCOPE*
34.23_8.16
39.05
44.8
31.02+
22.46 17.46641100 1.96
24.61 +3.99
MDGPT*
39.54_9.02
39. 19..24 2..92
45.39
13.58
22.35+3.77
23.68
MDGFX
44.83 7.41
42.18 6.44
46.84 46.84
40.77
24.30
28.36
UniPrompt(Ours)
45.37 =9.08
43.25
55.01 +3.36
51.5849.91
25.29 13.86
5.665
model. As shown in Figure 3] our method consistently outperforms existing GPL. baselines across most heterophilic datasets. Performance improvements are observed on Cornell, Texas, Wisconsin, and Actor, indicating that our approach avoids overfitting and makes use of the label information. On CiteSeer and PubMed, our method also outperforms existing GPL baselines, demonstrating its effectiveness when the dataset matches the homophily assumption of pretrained model. On Cora and Chameleon, the advantages of our method become more pronounced as more labels are introduced, gradually surpassing current GPL baselines. Similar experiments are conducted on GRACE and GraphMAE, further validating the generalization capability of our method. Detailed results and analysis can be found in the Appendix B.4
6.3 Cross-Domain Node Classification
To further demonstrate the broad scenario adaptability of our method compared existing approaches, we conduct experiments under both 3/5-shot and challenging multi-domain pretraining settings. In these settings, not only are the upstream and downstream datasets entirely different, but the multiple source domains within the pretraining also differ in both structure and semantics.
UniPrompt(Ours)
GPPT
GraphPrompt
GPF
GPF+
EdgePrompt
EdgePrompt
70
Cornell
70
Texas
70
Wisconsin
2 60
60
55
50
40
25
20
Squirrel
24
28
E 26
24
23
26
24
20
20
1
PubMed
CiteSeer
65
Accuragy 19
65
60
50
55
45
50
50
40
40
3
5
3
3
Number of Shots
Number of Shots
Number of Shots
Figure 3: In-domain node classification experiments over nine datasets under different shot settings using DGI as the pretrained model.

---

1-shot Cross-Domain Node Classification. As shown in Table 2 our method outperforms various existing baselines, and competes with or surpasses state-of saute-of-the-ae afrthheart GFMs. Our approach achieves improvement on the PubMed and Cornell datasets. This improvement can be attributed to introduce connections among semantically similar nodes. In scenarios such as PubMed, which has few classes, and Cornell, which is a sparse graph, our method enables more effective adaptation of the pretrained model. In contrast, on Squirrel and Chameleon datasets, where graphs exhibit low homophily and dense inter-class connectivity, the performance differences across methods are less distinct.
Table 3: Cross-domain node classification. Accuracy 3/5-shot node classification tasks over six datasets. Each column represents a test domain, while others are train domains. The best results are highlighted in bold, and the runner-up with underline. Methods with reported from 27
Methods
Cora(5)
CiteSeer(S)
Pubmed(5)
Cornell(3)
Squirrel(3)
Chameleon(5)
GCN
60. 30.15
45.54
57.82 57.8228.25
39.53=1a.17
21.61
22.09
GAT
59.79 13.89
50.4842.91
57 57.55
34.53213.01
20.11
1.58
DGI
56.76.11.28 11.29
42.67
54. 54.04
43.2225.84
20.23 11.12
27. 27.68.5.22
GraphCL*
41.79
47.05
58.50g7.38
32.77
21.18 20.98
27. 27.45
GPPT*
43.67
47.3146.98
40.47 110.17
3.1.69
22.14
28. 1
Gpp
51.21+11.44
56.90+s.84
58, 58.76
38.17 38.17
21.62
28 28,09
GCOPE*
+33.98
53.1844.47
57.74
48.21
21.37
25.50
MDGPT*
59. 59.64
52.71
58.65
35.18
21.42.4.18
18.1815.18
MDGFX*
64.56
61.24 4.882
63.50
49.56_6.92
23.00
30.5442.87
UniPrompt(Ours)
65.64_3.64 3.60 59.37
65.09
52.09
26.70=3.78
31.38
3/5-shot Cross-Domain Node Classification. As shown in Table the availability of additional labels significantly boosts performance compared to the 1-shot scenario. On Squirrel and Chameleon, method achieves notable improvements and outperforms existing approaches. Moreover, method maintains superior performance on sparse graphs like Cornell and few-class datasets like PubMed. In contrast, Cora and CiteSeer, which exhibit high homophily, most baselines, including supervised baselines and GFMs, perform well when more labeled data are available. The performance gap between our method and existing GPL baselines becomes less pronounced in these two datasets. Overall, our method still maintains ad advantage, particularly as a GPL approach, which demonstrates the effectiveness in adapting to pretrained models.
6.4
Hyperparameter Analysis
Cora
CiteSeer
PubMed
Cornell
Texas
Wisconsin
Chameleon
Actor
Squirrel
60
Accuracy (%)
50
50
40
40
20
20
0.99
0.999
0.9999 tau
0.99999
k
Figure 4: Hyperparameter analysis of 7 and for 1-shot node classification with DGI pretrained.
We conduct hyperparameter analysis on 7 and k under DGI pretrained and 1-shot settings. As shown in Figure for 7, a notable observation is that heterophilic graphs require lower values (typically converging to 0.999 or 0.9999) to achieve performance gains, which validates the necessity of the prompt graph. For homophilic graphs, performance stabilizes when > 0.9999, consistent with existing research findings 35 that these graphs align with the homophily assumption of the pretrained models. For smaller values (c.g., 7=0.99), only Cornell maintains performance while other datasets

---

degrade, highlighting the necessity of input graph and demonstrating the robustness against model collapse. When 7=1.0, no prompt graph is injected, corresponding the ablation of our method. For k, The performance gains are most pronounced on sparse heterophilic graphs, i.e., Cornell, Texas, and Wisconsin. For larger datasets Chameleon, Actor, and Squirrel, the prompt graph provides similarity information that improves performance. For Cora and CiteSeer, performance remains stable across k values. However, PubMed performance drops when reaches 50, which attributes to the effects of high homophily and limited classes.
7 Conclusion
In this work, we categorize existing Graph Prompt Learning (GPL) methods based on their mecha- nisms and conduct analysis of them. Through this analysis, identify key problem in existing GPL methods: the adaptation gap between upstream pretraining and downstream scenarios. We decompose this issue into two aspects: lack of consensus on underlying mechanisms, and limited scenario adaptability. Through motivation experiments and theoretical analysis, we reveal that the representation-leve prompt is fundamentally equivalent to fine-tuning simple downstream classifier. This primarily serves to adapt the pretrained model to downstream tasks, rather than unleashing its inherent capabilities. We propose a perspective: graph prompt learning should focus on unleashing the capability of pretrained models, and the classifier adapts to downstream scenarios. Based on our perspective, we propose Uni UniPrompt, a novel GPL method that adapts any pretrained models, which leverages coompt-genertte topology while preserving the original structure to unleash the ca- pability of pretrained models. We evaluate UniPrompt on a comprehensive set of datasets, including homophilic and heterophilic graphs, under few-shot learning settings. The results demonstrate that UniPrompt consistently outperforms state-of-the-arr baselines in both in-domain and cross-domain scenarios. Overall, our work provides new perspectives on the design principles of GPL., improving the fields of few/zero-shot graph learning, unifying downstream graph tasks, cross-domain graph learning, and promoting the development of Graph Foundation Models.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (No. 62422210, No. 62276187, No. 62302333, 6o.62372323 and No. 92370111), the National Key Research and Development Program of China (No. 2023YFC3304503), and the Hebei Natural Science Foundation (No. F2024202047).
References
[1] Xiangguo Sun, Jiawen Zhang, Xixi Wu, Hong Cheng, Yun Xiong, and Jia Li. Graph prompt learning: A comprehensive survey and beyond. arXiv preprint arXiv:2311.16544 arXiv, 2023.
[2] Chenyi Zi, Haihong Zhao, Xiangguo Sun, Yiqing Lin, Hong Cheng, and Jia Li. Prog: A graph prompt learning benchmark. In The NeurIPS Datasets and Benchmarks Track, 2024.
[3] Thomas N. Kipf and Max Welling, Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations, ICLR, 2017.
[4] Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. Graph Attention Networks. International Conference on Learning Representations, ICLR, 2018.
[5] Zehong Wang, Zheyuan Liu, Tianyi Ma, Jiazheng Li, Zheyuan Zhang, Xingbo Fu, Yiyang Li, Zhengqing Yuan, Wei Song. Yijun Ma, et al. Graph foundation models: comprehensive survey, arXiv preprint axXiv:2505.15/16 arXiv, 2025.
[6] Mingehen Sun, Kaixiong Zhou, Xin He, Ying Wang, and Xin Wang. GPPT: and prompt tuning to generalize graph neural networks. In Proceedings of ACM SIGKDD Conference Knowledge Discovery and Data Mining, SIGKDD, pages 1717-1727, 2022.
graph pre-training
10

---

[7] Taoran Fang, Yunchao Zhang, Yang Yang, Chunping Wang, and Lei Chen. Universal prompt tuning for graph neural networks. In Advances in Neural Information Processing Systems, NeurlPS, volume 36, pages 52464-52489, 2023.
[8] Huanjing Zhao, Beining Yang, Yukuo Cen, Junyu Ren, Chenhui Zhang, Yuxiao Dong, Evgeny Kharlamov, Shu Zhao, and Jie Tang. Pre-training and prompting for few-shot node classification on text-attributed graphs. In Proceedings of ACM SIGKDI Conference on Knowledge Discovery and Data Mining, SIGKDD, pages 4467-4477 2024.
[9] Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, and Xindong Wu. Unifying large language models and knowledge graphs: A roadmap. IEEE Transactions on Knowledge and Data Engineering, TKDE, 36(7):3580-359) 2024.
[10] Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, and Philip S. Yu. A survey on knowledge graphs: Representation, acquisition, and applications. IEEE Trans. Neural Networks Learn Syst., TNNLS, 33(2):494 -14, 2022.
[11] Hao Yan, Chaozhuo Li, Ruosong Long, Chao Yan, Jianan Zhao, Wenwen Zhuang, Jun Yin, Peiyan Zhang, Weihao Han, Hao Sun, et al. A comprehensive study on text-attributed graphs: Benchmarking and rethinking. Advances in Neural Information Processing Systems, NeurIPS. 1772222176666 2023.
[12] Yuhan Li, Peisong Wang. Zhixun Li, Jeffrey Xu Yu, and Jia Li. Zerog: Investigating cross- dataset zero-shot transferability in graphs. In Proceedings of ACM SIGKDD Conference Knowledge Discovery and Data Mining, SIGKDD, pages 1725-1735, 2024.
[13] Duo Wang, Yuan Zuo, Fengzhi Li, and Junjie Wu. Lims zero-shot graph learners: Alignment of gnn representations with Ilm token embeddings. Advances in Neural Information Processing Systems, NeurIPS, 37:5950-5973, 2024.
[14] Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu, and Jihong Guan. All in one: Multi-task prompting for graph neural networks. Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining, SIGKDD, pages 2120-2131, 2023.
[15] Xingtong Yu, Zhenghao Liu, Yuan Fang, Zemin Liu, Sihong Chen, and Xinming Zhang. Generalized graph prompt: Toward unification of pre-training and downstream tasks on graphs. IEEE Trans. Knowl. Data Eng,, TKDE, 36(1 1):6277-6250, 2024.
[16] Hao Liu, Jiarui Feng. Lecheng Kong, Ningyue Liang. Dacheng Tao, Yixin Chen, and Muhan Zhang. One for all: Towards training one graph model for all classification tasks. In International Conference on Learning Representations, ICLR, 2024.
[17] Haihong Zhao, Chenyi Zi Aochuan Chen, and Jia Li. A survey of cross-domain graph learning: Progress and future directions. arXiv preprint arXiv:2503.1 11086, arXiv, 2025.
[18] Zhe-Rui Yang, Jindong Han, Chang-Dong Wang, and Hao Liu. Graphlora: Structure-aware contrastive low-rank adaptation for cross-graph transfer learning. In Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining, SIGKDD, pages 1785-1796. 2025.
[19] Jiawei Liu, Cheng Yang, Zhiyuan Lu, Junze Chen, Yibo Li, Mengmei Zhang, Ting Bai, Yuan Fang, Lichao Sun, Philip S Yu, et al. Graph foundation models: Concepts, opportunities and challenges. IEEE Transactions on Pattern Analysis and Machine Intelligence, TPAMI, 2025.
[20] Yun Zhu, Haizhou Shi, Xiaotang Wang, Yongchao Liu, Yaoke Wang, Boci Peng. Chuntao Hong, and Siliang Tang. Graphclip: Enhancing transferability in graph foundation models for text-attributed graphs. In Proceedings of the ACM on Web Conference, WWW, pages 2183-2197, 2025.
[21] Yuxiang Wang, Wenqi Fan, Suhang Wang, and Yao Ma. Towards graph foundation models: transfcrability perspective. arXiv preprint 20030050330 arXiv, 2025.
11

---

[22] Yun Zhu, Yaoke Wang, Haizhou Shi, Zhenshuo Zhang, Dian Jiao, and Siliang Tang. Graph- control: Adding conditional control universal graph pre-trained models for graph domain transfer learning. In Proceedings of the ACM on Web Conference, WWW, pages 539-550, 2024.
[23] Xingbo Fu, Yinhan He, and Jundong Li. Edge prompt tuning for graph neural networks. In International Conference on Learning Representations, ICLR, 2025.
[24] Zemin Liu, Xingtong Yu, Yuan Fang, and Xinming Zhang. Graphprompt: Unifying pre-training and downstream tasks for graph neural networks. In Proceedings of the ACM on Web Conference, wwW. pages 117-428, 2023.
[25] Xingtong Yu, Jie Zhang, Yuan Fang, and Renhe Jiang. Non-homophilic graph pre-training and prompt learning. In Proceedings of ACM SIGKDD Conference Knowledge Discovery and Data Mining V. I, SIGKDD, pages 1844-1854, 1854, 2025.
[26] Xingtong Yu, Chang Zhou, Yuan Fang, and Xinming Zhang. Multigprompt for multi-task pre-training and prompting on graphs. In Proceedings of the ACM on Web Conference, WWW, pages 515-526, 2024.
[27] Shuo Wang, Bokui Wang, Zhixiang Shen, Boyan Deng, et al. models: Robust knowledge transfer via topology alignment. In Proceedings of International Conference Machine Learning, ICML, 2025.
graph foundation
[28] Petar Velickovie, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, and R. Devon Hjelm. Deep graph infomax. In International Conference on Learning Representations, ICLR. 2019.
[29] Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. Deep graph contrastive representation learning, arXiv preprint arXiv:2006. 200.:20000.,111,,, arXiv, 2020.
[30] Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, and Jie Tang. Graphmae: Self-supervised masked graph autoencoders. In Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining, SIGKDD, pages 594-604, 2022.
[31] Shantanu Thakoor, Corentin Tallec Mohammad Gheshlaghi Azar, Rémi Munos, Petar Veličković, and Michal Valko. Bootstrapped representation learning on graphs. In ICLR Workshop on Geometrical and Topological Representation Learning, 2021.
[32] Wangbin Sun, Jintang Li, Liang Chen, Bingzhe Wu, Yatao Bian, and Zibin Zheng. Rethinking and simplifying bootstrapped graph latents. In Proceedings of ACM International Conference on Web Search and Data Mining, WSDM, pages 665 673,667,,770 2024.
[33] Zhixun Li, Liang Wang, Xin Sun, Yifan Luo, Yanqiao Zhu, Dingshuo Chen, Yingtao Luo, Xiangxin Zhou, Qiang Liu, Shu Wu, et al. Gslb: The graph structure learning benchmark. Advances in Neural Information Processing Systems, NewrIPS, 36.30306-30318, 2023.
[34] Zhilin Yang, William W. Cohen, and Ruslan Salakhutdinov. Revisiting semi-supervised learning with graph embeddings. In Proceedings of International Conference on Machine Learning, ICML, volume 48, pages 40-48, 2016.
[35] Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang. Geom-gen: Geo- metric graph convolutional networks, In International Conference on Learning Representations, ICLR, 2020.
[36] Jitao Zhao, Di Jin, Meng Ge, Lianze Shan, Xin Wang. Dongxiao He, and Zhiyong Feng. Fug: Feature-universal graph contrastive pre-training for graphs with diverse node features. Advances in Neural Information Processing Systems, NeurIPS, 37:4003-4034 2024.
[37] Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. Graph contrastive learning with augmentations. In Advances in Neural Information Processing Systems, NeurIPS, volume 33, pages 5812-5823, 2020.
12

---

[38] Haihong Zhao, Aochuan Chen, Xiangguo Sun, Hong Cheng, and Jia Li. All in one and one for all: A simple yet effective method towards cross-domain graph pretraining. In Proceedings of ACM SIGKDD Conference Knowledge Discovery and Data Mining, SIGKDD, pages 4443 4454, 2024.
[39] Xingtong Yu, Chang Zhou, Yuan Fang, and Xinming Zhang. Text-free multi-domain graph pre-training: Toward graph foundation models. arXiv preprint arXiv:2405. arXiv, 2024.
[40] Alberto P García-Plaza, Víctor Fresno, Raquel Martínez Unanue, and Arkaitz Zubiaga. Using fuzzy logic to leverage html markup for web page representation. IEEE Transactions on Fuzzy Systems, IEEE Trans. Fuzzy Syst. 25(4):919-933) 2016.
[41] Benedek Rozemberezki, Carl Allen, and Rik Sarkar. Multi-scale attributed node embedding. Journal of Complex Networks, J. Complex Netw. 9(2), 2021.
[42] Derek Lim, Felix Hohne, Xiuyu Li, Sijia Linda Huang. Vaishnavi Gupta, Omkar Bhalerao, and Ser-Nam Lim. Large scale learning non non-homophilous graphs: New benchmarks and strong simple methods. In Advances in Neural Information Processing Systems, NewrIPS, volume 34, pages 20887-20902, 2021.
[43] Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. Open graph benchmark: Datasets for machine learning on graphs. In Advances in Neural Information Processing Systems, NeurIPS, volume 33, pages 22118-22111111 22118-2211 22133, 2020.
[44] Tomás Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space. In International Conference Learning Representations, ICLR Workshop, 2013.
[45] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint 1x07v:807.03777 arXiv, 2018.
[46] Philip Bachman, R Devon Hjelm, and William Buchwalter. Learning representations by maximizing mutual information across views. Advances in Neural Information Processing Systems, NeurlPS, 32, 2019.
[47] Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. Graph contrastive learning with adaptive augmentation. In Proceedings of the ACM on Web Conference, WWW, pages 2069-2080. 2021.
[48] Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. Graph neural networks: A review of methods and applications. Al open, 1:57-81, 2020.
[49] Yixin Liu, Ming Jin, Shirui Pan, Chuan Zhou, Yu Zheng, Feng Xia, and Philip S. Yu. Graph self-supervised learning: A survey. IEEE Trans. Knowl. Data Eng.. TKDE, 35(6):5879 5900, 2023.
[50] Yizhen Zheng, Shirui Pan, Vincent C. S. Lee, Yu Zheng, and Philip S. Yu. Rethinking and scaling up graph contrastive learning: An extremely efficient approach with group discrimination. In Advances in Neural Information Processing Systems, NeurlPS, volume 35, pages 10809-10820, 2022.
[51] Kaveh Hassani and Amir Hosein Khas Ahmadi. Contrastive multi-view representation learning on graphs. Proceedings of International Conference on Machine Learning, ICML, volume 119, pages
2020.
[52] Yuning You, Tianlong Chen, Yang Shen, and Zhangyang Wang. Graph contrastive learning automated. In Proceedings of International Conference on Machine Learning, ICML, pages 12121-12132, 2021.
[53] Susheel Suresh, Pan Li, Cong Hao, and Jennifer Neville. Adversarial graph augmentation to improve graph contrastive learning. In Advances in Neural Information Processing Systems, NeurIPS, pages 15920-15933, 2021.
13

---

[54] Jun Xia, Lirong Wu, Ge Wang, Jintao Chen, and Stan Z. Li. Progel: Rethinking hard negative mining in graph contrastive learning. In Proceedings of International Conference on Machine Learning, ICML, volume 162, pages 24332-243444 2022.
[55] Dongxiao He, Jitao Zhao, Cuiying Huo, Yongqi Huang, Yuxiao Huang, and Zhiyong Feng. A new mechanism for eliminating implicit conflict in graph contrastive learning. In Proceedings of the AAAI Conference on Artificial Intelligence. AAAI, volume 38, pages 12340-12344 2024.
[56] Hengrui Zhang, Qitian Wu, Yu Wang, Shaofeng Zhang, Junchi Yan, and Philip $ Yu. Localized contrastive learning on graphs. arXiv preprini arXiv:2212.0444 1.46644 arXiv, 2022.
[57] Mengyue Liu, Yun Lin, Jun Liu, Bohao Liu, Qinghua Zheng, and Jin Song Dong. B² sampling:: Fusing balanced and biased sampling for graph contrastive learning. In Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining, SIGKDD, pages 1489-1500, 2023.
[58] Jiaming Zhuo Can Cui, Kun Fu, Bingxin Niu, Dongxiao He, Chuan Wang. Yuanfang Guo, Zhen Wang. Xiaochun Cao. and Liang Yang. Graph contrastive learning reimagined: Exploring universality. In Proceedings of the ACM Web Conference. WWW, pages 641-651, 2024.
[59] Yongqi Huang, Jitao Zhao, Dongxiao He, Di Jin, Yuxiao Huang, and Zhen Wang. Does gel need a large number of negative samples? enhancing graph contrastive learning with effective and efficient negative sampling. Proceedings of the AAAI Conference on Artificial Intelligence, AAAI, volume 39, pages 17511-17518, 2025.
[60] Jaming Zhuo Feiyang Qin, Can Cui, Kun Fu, Bingxin Niu, Mengzhu Wang, Yuanfang Guo, Chuan Wang, Zhen Wang, Xiaochun Cao, et al. Improving graph contrastive learning via adaptive positive sampling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, CVPR, pages 23179-23187, 2024.
[61] Jiaming Zhuo, Yintong Lu, Hui Ning, Kun Fu, Bingxin Niu, Dongxiao He, Chuan Wang, Yuanfang Guo, Zhen Wang. Xiaochun Cao, et al. Unified graph augmentations for generalized contrastive learning on graphs. Advances in Neural Information Processing Systems, NeurlPS, 37:37473-37503, 2024.
[62] Wen-Zhi Li, Laang-Dog Wang, Hui Xiong, and HaanHHung Lai. Homogel: Rethinking homophily in graph contrastive learning. In Proceedings of ACM SIGKDD Conference Knowledge Discovery and Data Mining, SIGKDD, pages 1341-1352, 2023.
[63] Dongxiao He, Yongqi Huang, Jitao Zhao, Xiaobao Wang, and Zhen Wang. Str-gel: Structural commonsense driven graph contrastive learning. In Proceedings of the ACM Web Conference, WWW, pages 1 1129-1144 2025.
[64] Namkyeong Lee, Junseok Lee, and Chanyoung Park. Augmentation-ffre self-supervised learning on graphs. In Proceedings of the AAAI Conference on Artificial Intelligence, AAAI, volume 36, pages 7372-7380, 2022.
[65] Jun Xia, Lirong Wu, Jintao Chen, Bozhen Hu, and Stan Li. Simgrace: A simple framework for graph contrastive learning without data augmentation. In Proceedings of the ACM on Web Conference, WWW, pages 1070-1079, 2022.
[66] Dongxiao He, Jitao Zhao, Rui Guo, Zhiyong Feng, Di Jin, Yuxiao Huang, Zhen Wang, and Weixiong Zhang. Contrastive learning meets homophily: Two birds with one stone. In Proceedings of International Conference on Machine Learning, ICML, volume 202, pages 12775-12789, 2023.
[67] Xiaobao Wang, Jun Yang, Zhiqiang Wang. Dongxiao He, Jitao Zhao, Yuxiao Huang, and Di Jin. Graph contrastive learning with multiple information fusion. Expert Systems with Applications, ESWA, 268: 126129, 2025.
[68] Jingyu Chen, Runlin Lei, and Zhewei Wei. Polygel: Graph contrastive learning via learnable spectral polynomial filters. In International Conference on Learning Representations, ICLR, 2024.
14

---

[69] Guancheng Wan, Yijun Tian, Wenke Huang, Nitesh V Chawla, and Mang Ye. S3gel: spectral, swift, spatial graph contrastive learning. In Proceedings of International Conference on Machine Learning, ICML, pages 49973 2024.
[70] Thomas N Kipf and Max Welling. Variational graph auto-encoders. arXiv preprint 1611v:661..070000 arXiv, 2016.
[71] Zhenyu Hou, Yufei He, Yukuo Cen, Xiao Liu, Yuxiao Dong, Evgeny Kharlamov, and Jie Tang. Graphmae2: A decoding-enhanced masked self-supervised graph learner. In Proceedings of the ACM on Web Conference, WWW, pages 737-746, 2023.
[72] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. Advances in Neural Information Processing Systems, NeurlPS, 13:21271-21233 2020.
[73] Dongxiao He, Lianze Shan, Jitao Zhao, Hengrui Zhang, Zhen Wang, and Weixiong Zhang. Exploitation of a latent mechanism in graph contrastive learning: Representation scattering. Advances in Neural Information Processing Systems, NeurIPS, 37:115351-115376 2024.
[74] Junhyun Lee, Wooseong Yang, and Jaewoo Kang. Subgraph-Ievel universal prompt tuning. arXiv preprint arXiv:2402. 10380, arXiv, 2024.
[75] Jiazheng Li, Jundong Li, and Chuxu Zhang. Instance-aware graph prompt learning. Transactions on Machine Learning Research, TMLR, 2025.
[76] Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. Advances in Neural Information Processing Systems, NeurlPS, 30:6309-63 11330966318 18, 2017.
[77] Jaapeng Zhu, Zichen Ding, Jianxiang Yu, Jiaqi Tan, Xiang Li, and Weining Qian. RELIEF: reinforcement learning empowered graph feature prompt tuning. In Proceedings of ACM SIGKDD Conference Knowledge Discovery and Data Mining, V.I, SIGKDD, pages 2159- 2170, 2025.
[78] Fengyu Yan, Xiaobao Wang, Dongxiao He, Longbiao Wang, Jianwu Dang, and Di Jin. Hetergp: Bridging heterogeneity in graph neural networks with multi-viww prompting. In Proceedings of the AAAI Conference on Artificial Intelligence, AAAI, volume 39, pages 21895-21903, 2025.
[79] Qian Huang. Hongyu Ren, Peng Chen, Gregor Krzmanc, Daniel Zeng, Percy Liang, and Jure Leskovec. PRODIGY: enabling in-context learning over graphs. In Advances in Neural Information Processing Systems, NeurlPS, volume 36, pages 16302-16317,2002
[80] Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. Proceedings of the IEEF/CVF International Conference on Computer Vision, ICCV, pages 3836-3847, 2023.
[81] Xingtong Yu, Yuan Fang, Zemin Liu, and Xinming Zhang. Hgprompt: Bridging homogeneous and heterogeneous graphs for few-shot prompt learning. In Proceedings of the AAAI Conference on Artificial Intelligence, AAAL, pages 16578-1666 16586, 2024.
[82] Yihong Ma, Ning Yan, Jiayu Li, Masood Mortazavi, and Nitesh V Chawla. Hetgpt: Harnessing the power of prompt tuning in pre-trained heterogeneous graph neural networks. In Proceedings of the ACM on Web Conference, WWW, pages 1015-1023, 2024.
[83] Jiabin Tang, Yuhao Yang. Wei Wei, Lei Shi, Long Xia, Dawei Yin, and Chao Huang. Higpt: Heterogeneous graph language model. In Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining, SIGKDD, pages 2842-2853, 2024.
[84] Lecheng Kong, Jiarui Feng, Hao Liu, Chengsong Huang. Jiaxin Huang, Yixin Chen, and Muhan Zhang. GOFA: A generative one-for-all model for joint graph language modeling. In The International Conference on Learning Representations, ICLR, 2025.
[85] Zheyuan Liu, Xiaoxin He, Yijun Tian, and Nitesh V. Chawla. Can we soft prompt Ilms for graph learning tasks? In Proceedings of the ACM on Web Conference, WWW, pages 481-484, 2024.
15

---

[86] Edward Hu. yelong shen, Phillip Wallis, Zeyuan Alue Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. LoRA: Low-rank adaptation of large language models. In International Conference on Learning Representations, ICLR, 2022.
[87] Yufei He, Yuan Sui, Xiaoxin He, and Bryan Hooi. Unigraph: Learning unified cross-domain foundation model for text-attributed graphs. In Proceedings of ACM SICKDD Conference on Knowledge Discovery and Data Mining, V.I, SIGKDD, pages 448-459. ACM, 2025.
[88] Runjin Chen, Tong Zhao, Ajay Kumar Jaiswal, Neil Shah, and Zhangyang Wang. Llaga: Large language and graph assistant. In Proceedings of International Conference on Machine Learning, ICML, pages 7809-7823, 2024.
[89] Jiabin Tang, Yuhao Yang, Wei Wei, Lei Shi, Lixin Su, Suqi Cheng, Dawei Yin, and Chao Huang. Graphgpt: Graph instruction tuning for large language models. In Proceedings of International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR, pages 491-500, 2024.
[90] Zehong Wang, Zheyuan Zhang, Nitesh Chawla, Chuxu Zhang, and Yanfang Ye. Gft: Graph foundation model with transferable tree vocabulary. Advances in Neural Information Processing Systems, NeurIPS, 37:107403-107443, 2024.
[91] Zehong Wang, Zheyuan Zhang, Tianyi Ma, Nitesh V Chawla, Chuxu Zhang, and Yanfang Ye. Towards graph foundation models: Learning generalities across graphs via task-trees. In Proceeding of International Conference on Machine Learning, JCML, 2025.
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A Proofs
A.1 Proof for Proposition 4.1
(C's o (h) = Wo(Wh + by) = Wc) Wh Wy. Then, we let We W Wcc b = W c by, we can get (C+T)(h) = W bb. = C").. Therefore, we conclude that any linear prompt combination can be represented linear classifier with bias term.
Proof:
A.2 Proof for Proposition 4.2
Proof: For any parameters of objective function Wer and bo, there exists Wr and We:
Wc (W+) We by W
where W is the pseudo-inverse matrix of Wc. The mapping above is unique when W C
(w Wc) WT and We has full column rank. We calculate the gradient update paths of the two optimization methods respectively. For the original gradient of We and Wr, we have the following:
OL ÖL OC'
ØL
ØL OL OC'
aL
TWc c ac' oWc = (Wrh+ bt)
We
hJ. (10)
OC'
OWT T
O0 OW,
Oc
For ease of understanding, the matrix of two equations are Vw L = Whhh- + by) (Vo and Ww, = We Vc h", Then, the gradient of by is VD, L = We L. For the classifier C", we calculate the gradient of We and bo using Vw L = h- (Vo and Vb V L. According to W = W.W.c. and b = WTbr, we analyze the gradient propagation using the chain rule:
AWc W, (AWT) Tw
W (w + by). (Vc=L))) + wh LV W)
(1I)
1/h (V..) when W.Wo I and W WT la.
For bo, have the following:
Abe wT + (AWc) bt
(12)
We + (Wrh+ by) V.
when We has full column rank and by is orthogonal to Wyh bb,, we can obtain Abe = V which is consistent with the gradient of single linear classifier C².
B Other Experiments and Detail Settings
B.1 Experimental Setup
Implementation details. In our experiments, we use 2-layer GCN backbones for DGI and GRACE, and 2-layer GAT backbone for GraphMAE. For downstream prompt tuning, all classifiers employ 2-layer MLPs. We fine-tune all GPL baselines across all pretrained models. We train for 2000 epochs with early stopping (patience=20). Following the ProG [2] benchmark settings, we conduct k-shot sampling evaluations under both in-domain and cross- cross-domain settings with & E (1,3,5), To ensure performance reliability, we perform 20 repeated runs for each of fixed random seeds = { 42, 12345, 23344, 38108, 39788 ), reporting averaged results over 100 total trials. All of the experiments conducted on a server with Xeon(R) Platinum 8352V CPU, 90GB of memory, an RTX 4090 graphics card, and 24GB of video memory. The detailed GitHub links for the various pre-trained models, GPL baselines, sampling, split, and evaluation settings used in our experiments are provided in Table 12 which can be used for future reference and reproducibility.
B.2 Real-world datasets
We introduce the details of the 10 commonly used real-world datasets, including homophily and heterophily graphs follows, and the statistics of these datasets are shown in Table
24

---

Cora [34] CiteSeer
34 and PubMed [34 are citation datasets, nodes represent papers, edges represent citation relationships. Each dimension the feature corresponds a word. Labels are the categories into which the paper is divided.
Cornell 35
Texas
35 and Wisconsin 35 are sub-datasets WebKB 40 which is
webpage dataset collected from Carnegie Mellon University. Nodes represent web pages, and edges represent hyperlinks between web pages.
Chameleon 35
and Squirrel [35] page to page networks specific topic collected from Wikipedia 41, nodes represent web pages and edges represent links between web pages.
The average monthly traffic of the web page is converted into five categories predict.
Actor 35 is the actor-only induced subgraph of the ailn-irrector-aee cuirecto--actorvvee aeee--actot--iiieei writer network. Each node corresponds to an actor, and the edge between two nodes denotes co-occurrence on the same Wikipedia page. Node features correspond to some keywords in the Wikipedia pages. The task is to classify the nodes into five categories in term of words of actor's Wikipedia.
arXiv-yeur 42 is a modification of the OGBN-arXiv 43,, where the labels are assigned based on paper's publication year rather than topic. The nodes represent papers from arXiv website, and the links denote citation relationships. The node features are averaged Word2Vee [44] token features of both the title and abstract of the paper. The dataset is partitioned by publication date, which ensures a relatively balanced distribution of classes.
Table 4: Statistics of real-world datasets.
Dataset
#Nodes
#Edges
#Features
#Classes
#Homophily
Cora
2,708
5,278
1,433
7
0.81
CiteSeer
3,327
4,552
3,703
6
0.74
PubMed
19,717
44,324
500
3
0.80
Cornell
183
298
1,703
5
0.31
Texas
183
325
1,703
5
0.11
Wisconsin
251
515
1,703
5
0.20
Chameleon
2,277
36,101
2,277
5
0.24
Actor
7,600
30,019
932
5
0.22
Squirrel
5,201
217,073
2,089
5
0.22
arXiv-year
169,343
1,166,243
128
5
0.22
B.3 Descriptions of Various Baselines
Graph Semi-Supervised Baselines.
GCN introduces a spectral graph convolution framework based on localized first- order Chebyshev filters, utilizing mean-pooling for neighborhood aggregation. It recursively updates node representations by averaging the features neighbors and uses learnable parameters control the transformation process.
GCN B]:
GAT proposes multi-head attention mechanisms to dynamically compute node- specific weights during message passing. adopts a learnable attention coefficient to quantify the importance of neighbors, thereby achieving adaptive aggregation.
GAT 4
Graph Pretraining Models. We introduce the classic graph pretraining strategies follows.
DGI (28): Deep Graph Infomax (DGI) learns node embeddings by maximizing the mutual information (MI) between local node representations and graph representation. It utilizes GCNs generate node representations, and aggregates node representations into graph representation. DGI treats the corrupted graph as negative example and train by identifying the relationship between nodes and graphs, thereby maximizing MI between them.
CRACE [29]: GRACE learns node embeddings by maximizing mutual information between node representations two augmented views. generates different views through edge removal and feature masking. It uses InfoNCE 45 46 as loss function, which maximizes the similarity of two augmented nodes generated by the same node and minimizes the similarity of other nodes to train the model.
25

---

Table 5: In-domain node classification. Accuracy on 3-shot node classification tasks over three pretrained strategies and nine datasets. The best results in each pretrain strategy are highlighted in bold, and the runner-up with an underline.
Pretrain
Methods
Cora
ChteSeer
PubMed
Cornell
Texas
Wiscossia
Chameloon
Actor
Squirrel
Fine-tuming
65,00
610.22
41 88
38, 70
41.34
28.0
22.01 1230
23.00
Linear-probe
07 28.48_
00.0
B5-92
40.30
30. 30
58.20
27 22
20 11:00gms1
22: 56.56
DPPT
52.75..6 3g
16.07
50 58
57. 25
34 14.0
S5.80
22, 22.71
19.70
21
GraphPrompt
64.29
51.57
80 60:56
25
36.
29.
20.02
DOI
OFF
64.97.7444
05.77
28.52.1.57
00.82,1.7
02.94
W7
52.
30.37
38 18,00
47.04
24 24.44.6
24 46.76
26. 15
21.
21.84
22.01-7.0
2.00
64.52.1.9
34.
5%
48
EdgoProapt
41 18.77
25. 14.7
21.45:1.33
EdgePrtept+
10
00.25
60
37.01 a c4
45
22.00,a.s
...90
09.07 44.37
61.73 15
00.94.5 45
00.00
00.033.5.4
08.70 61.44:a14.5
23.00
27. 17.35
24.19 up.35
Fime-Luning
63.900 60
MB 4.32
08 10
22.42 46
39: 73.7444
a0.34.5
7344.
28.77 a..00
probe
03.68_.. MM
00.35 19
600 19
30 1.5551.m
18 05 n
20 17022amm so
21 35.5 se
11.60
GPPT
54.24_7 29
51.77 13.30
33. 23.55
37 15 cs
31 31.45
20.78
26. 17 27:17
GraphProupt
67.00
53.84:5.22
34.8225.44
29.06
32. 11.46
20.98
GRACE
OPF OPF
All-in-omw
64.02
00.78 01
31.00
62.45 62.47.49
61 50
88 26 so
That
41. 41.7645:75
30. 15
21 20.40.mm
25. 4
21.61 21
21 21.32
25. 3..50...0
21.
23.24
55 68
St. 10
41 11.37
56 63
59 52.0
19.07 19.67
25. 57
EdgeProapt
00.45_..30
17 33
12.58 12.58.1us
42 12.97
35 46
27, 11532
21 611611
23 23.14.2211
EdgoProapte
61 61.00_2..
15.12
12:38
12 11
G1 13.83
59
27 82
21 11.00
25.37
Uni UesPesnpt(Urrs)
07.77 07.71a5.3
,1
66.88 66.88
00.86...37
64.22
67.00...57
27. aaa.ee
25.5
Fime-taning
06.38
08.77
02 51
16.00
13.91
48.31
27. 17
21 1.
21 31.188..00
Linear probe
70.74
00.00,+1
86. 94.77
56. S5.52.m48
13.15 10.15
41 10:5
20.02.10
22.08 17
21 21:91
GPPT
57 64
10. 10.14...0
56 63
S5. 12
38. 15.54
63.01.53
27. 12237
20:0
20.58
GraphProapt
67.49
57.51
62:78..77
23.79.702
29. 29.76118:13
27.90:a35
21 20.02
21.5012206
26.29:2.34
GragkMa
AnF
30.30-44.0
67.51
48.64.32mm
54
27
18
96, 23
24
38 43
16
28.90.15.
21.40a774
27.09
21.301
20
56.55.s 74
25.50um
35, 37.274-
20.50 51
EdgaPrompt
64:18_...0
67.56.1.4
54
12.17
40.05
25.31_500
2..00
19:00
220061127
EdgoPesapt+
64
50.06.1.12
23308
k7.20l
41.00aa:33
38.50..m
22 2.1002287
20.59.4445
21.7220000
10.166.5.00 60
61.90.a.t. sia
64.02 15
09.92..5.0
65.02
71.00.2.4
27.78
22.53 24.77
GraphCL 37:: GraphCL learns graph-level representations by maximizing mutual informa- tion between augmented views of graphs. It introduces four graph augmentation types (node dropping, edge perturbation, attribute masking, subgraph sampling) to generate augmented views. The InfoNCE loss maximizes similarities between positive pairs (augmented views of the same graph) while contrasting against negative pairs (other graphs in the batch), corresponding to mutual information maximization between augmented representations and unifies diverse contrastive learning frameworks.
GraphMAE [30]: GraphMAE is a generative self-supervised graph autoencoder that learns robust representations through masked feature reconstruction. employs two-stage framework: (1) A GNN-based encoder learns node embeddings from input graphs with randomly masked node features; (2) A GNN decoder reconstructs the masked features using re-mask decoding strategy, optimized by scaled cosine error loss that emphasizes directional alignment over magnitude.
Graph Prompt Learning Baselines.
GPPT pioneers the use of the "pretrain-prompt" paradigm in graph machine learning. It employs link prediction its pre-training task to learn general knowledge about graph structures. In its prompt design, GPPT introduces two types of prompts: task tokens and structure tokens. The former serve as prototype vectors for each class within clusters, while the latter are derived by aggregating information from the target node and its neighbors. For downstream node classification, the task is reformulated as link prediction. This is accomplished by calculating the probability of a link existing between the task token and the structure token, thus leveraging the learned prompts to make predictions.
GPPT 6
GraphPrompt proposes unified pretraining and prompting frame- work for GNNs, bridging the gap between pretraining and downstream tasks through a subgraph similarity- -bsed template. It introduces learnable task-specific prompts that guide the ReadOut operation to dynamically emphasize task-relevant features during subgraph representation aggregation. By mapping both link prediction (pretraining) and node/graph graph classification (downstream) tasks to subgraph similarity learning, GraphPrompt enables parameter-efffiient adaptation via prompt mming-freezing pre-trained GNN weights while optimizing lightweight prompts.
GraphPrompt 124]:
All-in-one [14 All-in-one unifies the downstream tasks of the ppretrain-prompn paradigm. This method first reformulates node and edge tasks into graph-level tasks by
26

---

Table 6: In-domain node classification. Accuracy on 5-shot node classification tasks over three pretrained models and nine datasets. The best results in each pretrain strategy are highlighted in bold, and the runner-up with an underline.
Pretrain
Methods
Cora
CiteSeer
PubMed
Cornell
Tesas
Wiscousin
Chamelooa
Actor
Squirrel
Fine- tuming
73.01
65
20.01
15 20.78.a.. es
45 23
43. 26
28. 28.81
23, 22
22 08
Linear probe
72.39 22 c
65.11.2242
70.32:4-39
45. 05.25
42
22,
20.60
GP97
67.78
51 61.6445.0
64.50
42. 42.1944.54
41 47.37
23.87
21
GraphPeoopt
22.35
31 20
20.38 2m
DOI
ALl-15
66.67
s..c
613
28.65
16.52
34.02.x.c
32. 19...
42.00
42.08 m
22 21 s
20, a.
23.38
GPP+
68: 10:0
67 65
68.81 24
83.08
49 44.41
EdgoPrompt
06. 12.82aa ep
56. 24.00
64.08
15.14
40 10.10
47 01.11a
21.82
21, 62 no
EdgaPtaapt-
67. 27..00a.. 04
26.12
46 88.8
50.4
24. 43.56
21. (1 Loo
21.64
Un (uar)
70. 58
AB p6.10
20.97
68.02211.0
67.80 oo
70.433
25 04
28.20 6s
27. 80.888--5
Fine- biiingg
70. 09 19.2..
64. a3.4
70. 15.32
47 17.15
43 13.09
42.51 us.50
$4.001244
22 61 a..0
25, 25.22..
Lizear- probe
71 01
61
7131.
47.17a
42.13as.::
41 41.91:600
32 12.778.3115
22 221221
24.05
GPY?
56.51:
60.8545.6
66,97
44.9614
41. 15aaa
41 41.98g7
35. 2..47
21. 22
24. 70
drephPrompt
68. 24.58
52 2584
15, 25.28
38 15.65
33. 70
21. 17
22.55
GRACE
In-oze
68.
44.29 u.
20.27
50.53
40.755.
22.
21. 01..77.
21
125.
GPP+
68. 25
61.51
10.90
22. 24.
24.61 22:24
EdgePrompt
63.76
11.8
68.25 66.25
18.17
54.55a7a
23. a1:00
24.22
G6. 1G os
7Lilk.a in
17.8
46. 11..0
21.46 Lis
24,35
PniPProppt(orry
72.00 as
63.644a
74.21 2:
68. 15
68.30g1m
71 41.43.4.5
33.66
151
28.08 67
20.07 20
73. 25
64. 44.30
72.83 1.21
28.24
47 17.62
50 20.00
78, 28.4..7
21. 22
22.38
Lizear- probe
75. 78
66.17
70.08 69
45.000
41. 41.11,770
81.331 01.31
22 22.51.
22,25
G9Y7
64. 64.06
26.87
62. 22
46. 24.
41.04
46. 10
26.48_1:12
20, 14
20.97
PropaProept
60. co
40.1774410
67 67.51
25, 13
38 54.544..
30 30.600
25.65 23.65.2247
20 06
24. 71,
GraghMMR
aPP
All- In-ose
72. 72.09:a
41.106.634
62.75gam4
41.9773.63
68.5615.10
65.474445
27.38 387.333
41 16
20.13as:::
45 43.0845.3
31. 31.50ia17.40
42 m0
23.50.3..4
26.51
21. al
22 22.2
26.14 aa..M
21 04
63. 60.25
55.6025.00
65.95.8.5
48.53.7.c
38.61a848
42.38
20, 20:80:1m
21.12
67.74.3.c co
62.2943.54
68-6146636
44.3744.37
48.58a5 44.02274s
20.35.2..0
22.
21. 21.68
48.204400
65.01
44.122
46.561781
42.14
29.81 2::5
21. 21.74.
21. 21.38
Purs)
74.774499
65.74
70.0044.9
67.75...m
71.02.121
73.89....1
20.77.2.00
24.9653..8
20.95_g1 m1
constructing an induced subgraph. It then generates learnable prompts and integrates them into the node features in weighted manner to construct prompt graph. Furthermore, this method combines meta-learning to optimize prompts across multiple tasks and make the prompts adapt to different downstream tasks.
CPF/GPF-plus 77 GPF GPF/GPF-plus framework introduces a universal graph prompt learn- ing method that is compatible with any pre-training strategy. The approach operates by adding learnable prompt vectors to the input node features. Specifically, GPF uses a single global prompt vector shared by all nodes, whereas GPF-plus generates individual prompt vectors for each node by aggregating basis vectors via an attention mechanism. The resulting prompted nodes are then fed into frozen pre-trained GNN for the downstream task. This method effectively overcomes a key limitation of existing prompt-tuning methods, which are restricted to specific pre-training tasks.
EdgePrompt/ httprrrrepp//ddeeeerrreeee Edgeprompt----r pplus [23]: EdgePrompt introduces a graph prompt tuning framework for pre-trained GNNs by injecting Iearnable edge-wise prompts into adjacency matrices. It designs edge-specific trainable vectors customize message aggregation patterns between nodes. This structural adaptation bridges the objective gap between pretraining and downstream tasks while preserving GNN parameters. EdgePrompt+ enables each edge to learn its customized prompt vectors, which is similar to GPF and GPF-plus.
Multi-Domain Graph Pretrain Baselines.
GCOPE proposes a cross-domain graph pretraining framework that unifies diverse graph structures by introducing learnable "coordinators" to align various datasets. These coordinators interconnect isolated source datasets into a unified large-scale graph, enabling joint pretraining with objectives. During pretraining, GCOPE learns transferable representations by balancing shared multi-domain knowledge and domain-so -omain-specific features through latent alignment strategies. The framework supports flexible transfer via fine-tuning or graph prompting while maintaining parameter efficiency.
GCOPE [8
MDGPT introduces prom-pr framework for downstream adaptation: a unifying prompt transfers broadly learned cross-domain knowledge by aligning target domains with the pre-trained prior, and a mixing prompt enables fine-grained domain- specific alignment through learnable projections. MDGPT bridges pretraining and downstream tasks by optimizing domain-invariant representations via self-superviee objectives on multi- domain data.
MDGPT 39
27

---

Table 7: Analysis of key components in UniPrompt via replacement experiments on I-shot, 3-shot and 5-shot node classification tasks over different pretrained models.
Precrain
Strutegles
Cora
CiteSeer
PahMed
Cornell
Texas
Wisconsin
Chameleon
Actor
Squirrel
Topo
a
62,8 a0.81
22,00 22.05 12.74
S4.7
21
21. 21.48,2-:
DGI
Binple,Ado
24,21
4.26
11.58-44.66
20.87
2..
24 ao0
Lascard__Tppo
27.27
28.00 96
14.94
02.74
23.88 en
26,99g 4
23 25.37
Randoe, Topo
17.28
29.00..
22.72.
27.64
23.63...1
24 ase
CRACE
Bingle,
50.74
18.70....4
00.30....0
21.06
49.8811.00
23.2221441
23. L.e
Discard,111 Topo
39.20 m9
16.18 us
20 en
58.44 m
29.84
5189
21.40g1 90
28.48
23 23.85gi1.
Randoe, Topo
18.0 4.44
28.33_724
34.53
10.15
19.30
20 10.5
DragaMM
Simple, Add
49.11-a in
57.79km to
49.38-111 on
42,97
25.47.1
21
00.00
DiscorrdTTop
48.01 1
18,99, 11.00
18883 as
10.00
44.024
13.4 i
28.77
19
Randoe, Topo
06.20 mn
65,50l an
28 cn
44.74
20 cn
49.3
co
DGT
Simple,Adh
67.77 i
88
24.77 1
1.
22.7
Discaari_Topo
27
45.05
62.19 00
55.63
00.00
26.78 44
21.71
23 12 2o
Randon_Tupp
64.78_a.0
40.47 64
25, so.ce
GRACE
Sinple,Aad 18.
31.41
25.00
23.47.1.31
24
DiscarddTopo
g
3827g
50.22 17
65.9441 37
y2 to
24.7
25, 20.20 o1
Randon_Tupp
2..3
83.76 m
11:60
21.71
20 sacce
GeaghMME
Bingle,Add
6M.77 in
68.08 is
59.4 06
87.50gm on
2481 to
25
200 00.52211
Brecardd_Tppo
61.72g..m 48
62 10gins
56.8gg 10
60:00g5 94
45.78
57.7 5771
26 16.llgh 46
24.65
21 12
Tandon_Tupo
66, 26.78_...0 or
68.27
40.57
26.09 77
10.80 et
19 10.34
bat
And
21/07
65 is
2291
57
09.57227
20.74
25.62 to
25. 15
21 20g
Bixcarrd_Tepp
37.53__.21
40.75.2..0
01.5%
00. 06.72__29
60 20.22,33.15
24.062.04
28.38 44
28.04
22.6
5
GRACR
Biddle_AAd
Topo
60. 53_. no
60.00 00.91_2..0 an
61.02 30
51.47
5000
70g es
10.50_. 0s
10 as
63.9 aa
33,00g so
2800 cs
23: 19
23. a
25 11
21 10.50g
Discard_ Topo
61. 61.31__2.0
53.50
37.02
07.57_22.4
69.84.
70.29
2901
25.08.0:54
24.50 24.50
Randon, Tapo
47. -1 in
10
57.05 e1
38.50_2 m
39.57 in
$8.03
22.02 cs
19.09
20 20
GraghMAR
Simple
69. 72 2:02
26.06:3.02
73.22
67.19_1.4 40
66.72.s.8
74.74
28.12.a0
23.21
23 25
Tapoo
30.12:.4m0
61.06
68.70 68.70.224
06.25_g144 40
68.75g4.0
75.45
29.3422m
20.59,1.44
23.06gtmm
: MDGFM integrates multiple source domains during pretraining, leveraging con- trastive learning to maximize mutual information between multi-view graph augmentations. The topology-awan refinement process, which aligns different graph topologies into unified semantic space via meta-prompts (for global knowledge transfer) and task-specific prompts (for domain adaptation).
MDGFM [27]
B.4 3/5-shot node classification on different pretrained models
We further conduct 3-shot and 5-shot node classification experiments on the same nine datasets, based on three different pretrained strategies, as shown in Table and Table Consistent with the 1-shot results, our method outperforms existing GPL approaches across most datasets under different pretrained model settings. However, the baselines become more competitive in these scenarios, with each achieving runner-up on certain datasets. Another observation is that, as the number of shots increases, the performance discrepancies among models under different pretrained settings become more pronounced, especially on datasets such as CiteSeer and Chameleon. In comparison to the GPL baselines, UniPrompt demonstrates more stable performance across all settings. It is also noteworthy that with an increase in the number of labels, Fine- and -probe become highly competitive, achieving runner-up even optimal performance on many datasets. This indicates that traditional fine-tuning methods, much like GPLs, benefit significantly from additional label information and, in some instances, utilize it more effectively than some existing prompt approaches. Furthermore, our method achieves particularly significant gains on the Cornell, Texas, and Wisconsin datasets, with this strong performance holding consistently across the different pretrained models. This further underscores the broad applicability and robustness of UniPrompt.
B.5 Key Components Analysis of UniPrompt
To further analyze the pros and cons of each component in UniPrompt, we initially aimed to remove the key components (i.c.. ANN and bootstrap). However, since both components are essential and cannot be simply removed, we instead conduct replacement experiments: (I) Random_ Topo: replacing kNN with random topology, (2) Simple Add: replacing bootstrap with a simple addition of the original and prompt graph, and (3) Discard_ Topo: discarding the original graph totally. The 1-shot results are shown in the Table 7 From the table. we can find that Random_ Topo maintains some of effectiveness on homophilic datasets while showing reduced performance on heterophilic ones. Conversely, for Simple_Ada and Discard_ Topo, heterophilic datasets still retain some performance. However, performance on homophilic datasets drops significantly, as their original structure is crucial for classification. Furthermore, notable phenomenon is that when these core
28

---

Table 8: In-domain large scale node classification. Accuracy 5-shot node classification tasks over three pretrained models and arxiv-year dataset. runner-up with an underline.
The best result highlighted in bold, and the
Pretrain
Method
ar XrX-vyyear (Ace)
Preprocessing Time (s)
Training Time (s/per_epoch)
Pine-tune
28. 26.97
0.0138
DGI
UniPrompt
32.48
1.25
0.0224
Fine-tune
24.60#1.01
0.0205
GRACE
UniPrompt
25.17 25.17
1.26
0.0320
Fine- Fine-tune 23.2441..0
0.0427
GraphMAE
UniPrompt
24.25
1.32
0.0618
Table 9; Time (s) and GPU memory (MB) costs of different GPL baselines across various datasets.
Methods
Time/Memory
Cora
CiteSeer
PubMed
Cornell
Texas
Wisconsin
Chamelcon
Actor
Squirrel
Time
0.0027
0.0026
0.0033
0,0026
0.0025
0.0031
0.0026
0.0027
0.0034
Fine-tune
Memory
80.5
153.1
310.3
26.6
26.6
28.4
118.4
155.4
368.9
Linear-probe
Time
0,0010
0.0009
0.0009
0.0009
0.0009
0.0009
0.0011
0.0009
0.0009
Memory
56.9
127.5
120.1
26.4
26.4
27.6
70.5
86.9
122.0
GPPT
Time
0.0118
0.0117
0.0130
0.0123
0.0120
0.0119
0,0109
0.0051
0.0127
Memory
80.6
153.1
311.7
26.6
26.7
28.5
118.4
155.1
365.
GraphProupt
Time
0.0035
0.0008
0.0101
0.0242
0.0037
0.0136
0.0003
0.0006
0.0024
Memory
896.1
2366.2
37.0
1170.4
3597.7
Time
0.7616
0.7362
0.6024
0.6846
0.6732
0.6846
0.6856
0.7118
0.6834
All--in-one
Memory
2696.0
4021.5
9052.5
18018
4917.2
Time
0.0021
0.0031
0.0031
0,0020
0.0021
0.0020
0.0031
0.0022
0.0042
GPF
Memory
470.1
29.9
193.4
662.4
OPP+
Time
0.0033
0.0032
0.0033
0.0021
0.0023
0.0021
0.0032
0.0022
0.0042
Memory
470.5
662.6
Time
0.0018
0.0027
0.0042
0,0025
0.0017
0.0017
0.0041
0.0031
0.0127
EdgePronpt
Memory
746.2
36.3
379.8
2608.3
EdgeProupt*
Memory
Time
0.0024
191.5
0.0027
398.3
0.0042
746.2
0.0025
33.2
0.0024
32.0
0.0024
36.7
0.0040
551.0
0.0035
380.8
0.0122
2608.4
UniPrompt(Ours)
Time
0.0054
0.0052
0.0039
0,0040
0,0045
0,0039
0.0047
0.0068
0.0073
Memory
511.5
674.5
566.2
55.1
67.7
$39.9
1392.6
1603.9
components are replaced, an increasing number of labels does not consistently lead to performance improvements. This is particularly evident in the Random, Topo setting. Although this setup is analogous to augmentation strategies [47 37 in graph self-supervised learning, adding edges, as opposed to masking them, can introduce unnecessary message passing and additional potential risks. Thus, simply using random topology is clearly suboptimal. Another observation is the varied impact of discarding the original topology across different datasets. On homophilic graphs, performance drops significantly, whereas in heterophilic scenarios, performance is maintained on some datasets (i.e., Cornell, Texas) or even improved (i.e., Wisconsin, Chameleon). This suggests that the original topology in these latter cases fails to provide effective message passing mechanism, and useful information is instead derived by learning the distribution of representations around anchor nodes. In contrast, Un UniPrompt augments the graph with learnable topology, facilitating effective message passing for downstream adaptation and thereby ensuring good performance on both homophilic and heterophilic datasets.
B.6 Large Scale Dataset Node Classification
We additionally run experiments on the large-scale heterophilic dataset Arxiv-year as a supplement. Here, we use simplified KNN by randomly sampling 1,000 nodes, then connecting each node to its top-k most similar sampled nodes. We test three pretrain strategies under 5-shot setting, comparing with fine-tuning. The accuracy and computational cost are shown in the Table 8 Our method incurs minimal preprocessing time and only a slight increase training time per epoch, with small epoch counts (typically less than 500). This demonstrates that approach is scalable to large graphs
29

---

Table 10: Robustness analysis of the various pre-trained models to varying levels of Gaussian noise on I-shot, 3-shot, 5-shot node classification.
Pretrain
Shot
Noisy
Corn
CiteSeer
PubMed
Cornell
Texas
Wiscondin
Chameloon
Actor
Squirrel
0.01
a3.42.13.40
61.000a5.11
60.62
46.72a12.s
82251ase.s
20.80
2..9
22. 22250
0.05
23.28
10.77
40.01
40 40.22221282
59, 38.75
61 61.05gaa.7
20.72aa
24.67=2.0
20. 41 20.74
0.20
2.38
15.73
30.46
25.50
33.91
g10.4
42.86g16.31
20.05.0
22.1
20.21
0.01
63.72-4.32
27.00
63.33
52 02
45.31 45.31
56.80
25.2 25.27
24.58-2.7
18.6
29.72 4672
65.54 45.10g
7
19. 19.39
24.47 =3.0
20.08
15.38 44
g4.0
28.09 2a.2
48.75us.44
12.11 34.081221.00
15.99
22.15
19.99
0.01
22.30 22
66. 07
66.0 g.
47 as
68 40 as
21.5 2n
25.61
20 20 75
5
0.05
17.50 2.00 71
17.27
42. g2
63.75g444 45
19.38 P9
67.77
20. 20.41ga
19.79gg 20.5dg35: in
0.20
22.98
18.12s3.0
32.08
59 24.69
63.59
59 10.54psm
20.03
22.10:2.56
20.63
0.01
39.71
16.00
65. 10
47 g4g4g
27.97 21
50 20
27. on
23.08 24.12
0.05
20.92
16.51
46.31 16.31
19. 19.06. 40
27
38. 38.40 24,34
25. 05
19.00 19.62 2..02...:3
22.
13.87 20
g2.01
52. 15
$1.72 AP
20.00
26.17
20
18. 18.44g4.
21.21gi
GRACE
0.05
61.76
22.17
21 63
61.58
Bi 16.00....00 50
56 c..5.
gt..0
1..00 21.44
58.50
89 29.05
60. a4s.u
21 11.55a1 as
22. 42
22.94
20.5 23.25
20, 20.78
25. 20..7
0.20
17.08-7741
23.22g 14
40.0%
30.06 34
50.57 30.57
71.31
20.20ga
21.13
20 g0
0.01
69, 29.50.22.0
57.83 67.80
13.58 1a.50
61.72a5
55 65,00g
29.9 29.97a275
21. 20.14..1.0
0.05
20.98
19.9 19.9
60. 60.38
63.75g.1.4
61.61
66 66.20gs
25. 23.37
18.96_
21.3 21.31
0,20
6.80
17.05
55,34
60.51 60.51
51.25
60.69
17.47
18.02
19.01
10.25 10.25=s..e
30.03
56.5 a2
50.9 50.9
64.80
21. 21.85-9.52
12
10.58 47
18.32 20
20. 47
M0000 42
45
63.54
19 19.49 18
20.80 29
20-16 as
0.20
10.50
28.73
32.69 2s.se
g5.33
48.1 08.13
57.03
18.97a1 as
21.36.2.33
20. 20.44
CragaMAE
3
0.01
0.05
30.90_21445
67.07 47
43.01
16.63 16.00gg..1
65.04gamm
30.69
58, 20.53
60.31 51 am
53.91g10.57
55.3 g0
56,
57
19. 19.41.1 56.43g1245
25-4520 57.04
26.700_...0
26. 23.52
10.07 20
19.99
0,20
14.50=4..7
18.24
28.54
55.00
55.4 55.44
58, 58.40g11.45
19.45
20.77
20.13
0.01
60.04..3.70
4.58
_1.46_a..3
BT. 07.06
06. 66.58
25.01 24
24.19_
28.31
31.30
20.61
18.40
65. 65.78
74.29
22.30
20.11
19.02
0.20
14.87g5544
37.75g4416
11.25gg..1
54.22g11
74.74g301
19.83gr 79
17.406_5.7
19.84
B.7
Computational Overhead Comparison
we provide the 1-shot time and space table for various baselines of DGI pretrained models, are shown in the Table 9 In the table, UniPrompt demonstrates efficient performance in both time and GPU costs. In terms of inference time, our approach is comparable to all GPL baselines. While our use of kNN slightly increases GPU memory usage, it remains within an acceptable range. This indicates that our method is lightweight and can be quickly deployed on various datasets, showcasing its efficiency.
B.8 Robustness Analysis
Since our prompt topology is built on node features, it is sensitive to feature noise, which can lead to distorted graph structures. When both features and topology are misaligned with the pre-trained model, our method faces challenges in solving this problem. The I-shot learning results under varying levels of Gaussian noise are summarized in the Table I0 where we observe that 0.01 noise level shows minor augmentation, maintaining accuracy in some datasets (c.g. PubMed, Wisconsin and Actor). However, 0.05 noise begins to impact performance, and 0.20 noise significantly degrades accuracy across most datasets, with an average accuracy drop of over 30%.
B.9 Hyperparameter Settings
We conduct extensive experiments explore the impact of various hyperparameters on the perfor- mance of our model, as shown in Table [I] ensuring that our approach achieves robust and consistent results across diverse settings.
C Related Works
Graph Pretraining. Graph pretraining has emerged powerful paradigm for learning general- izable and transferable representations from large-scale unlabeled graph data, aiming to mitigate the dependency on labeled data in downstream tasks. Unlike traditional supervised methods that
require extensive manual annotations B 4 48), graph pretraining leverages self-supervised strategies to capture structural and semantic patterns in graphs. Graph Self-Supervised Learning (GSSL) [49] currently has attracted widespread attention in the graph community, which mainly designs self- supervised objective functions to train the model based on maximizing Mutual Information (MI).
As classic paradigm, DGI (28) maximizes mutual information between node representations and
30

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Table 11: Hyperparameter settings of Uniprompt for 1-shot, 3-shot, and 5-shot scenarios across
different pretrained models
Pretrain
Dutuset
1-shot
3-shot
5-shot
up_ Ir
down Ir
k
up Ir
down Ir
k
up.
down Ir
Cora
0.001
0.05
50
0.99999
0.0005
0.05
10
0.9999
0.0001
0.05
10
0.99999
CiteSeer
0.0005
0.05
50
0.9939
0.0005
0.05
10
0.9999
0,0001
0.05
10
0.9999
PubMad
0.0005
0.9999
0.0001
0.05
0,9999
0,0005
0.05
0,99999
Cornell
0.001
0.0005
50
0.99
0.001
0.01
50
0,9999
0.00005
0.001
50
0.9999
DGI
Texas
0,00001
0.0001
50
0.999
0.0001
0.00005
0.9999
0.00001
0.0001
50
0.9999
Wisconsin
0.0001
0.001
50
0,999
0.005
0.0001
50
0,9999
0.00001
0.0001
50
0.9999
Chamelcon
0,00005
0.001
10
0.9999
0.00001
0.05
10
0.999
0.00001
0.05
10
0.999
Actir
0.001
0,999
0.00001
0.01
0,9999
0,0005
Squirell
0,00005
0.005
50
0,99999
0.0005
0.01
50
0.99
0.0001
0.0001
50
0.9999
Cora
0.001
0.05
0,9999
0.05
CiteSeer
0.005
0.001
50
0.9999
0.00001
0.05
50
0.9999
0.00001
0.05
50
0.9999
PuhMad
0.01
0.05
0.9999
0,01
0,05
0.9999
0.01
0,0001
0.9999
Cornell
0.0001
0.0005
0.99
0.00001
0,0001
0,9999
0.00001
0,0005
0.9999
GRACE
Texas
0.0001
0.03005
50
0.9999
0.000055
0,0001
50
0.9999
0.00005
0.0005
50
0.9999
Wisconsin
0.0001
0.01
0.999
0.0001
0,0005
0.999
0.0001
0.0001
0.9999
Chameleon
0.005
0.001
0.99999
0.001
0.001
50
0.9999
0.005
0.05
50
0,99999
Actor
0.0005
0.01
50
0.9999
0.008
0.01
50
0,9999
0,005
0:05
50
0.9999
Squimel
0.9999
0.005
0.05
0.99999
0.005
0.05
50
0,99999
Cora
0.0005
0.0005
50
0.9999
0.0005
0.05
1
0.99999
0,005
0,0005
0.9999
CiteSeer
0.99939
0.801
0,9999
PubMad
0.005
0.01
0.999
0.0001
0.05
10
0.9999
0.0001
0.05
0.9999
Cornell
0,00005
0,05
50
0.9999
0.000055
0,005
50
0.9999
0.00005
0,0005
50
0.9999
GraphNAE
Texas
0.00001
0.0005
50
0,9999
0.00005
0.0005
0,9999
0.00005
0.0005
0.9999
Wiscomsin
0,00005
0.01
50
0.9999
0.08001
0.00005
50
0.9999
0.00001
0.0005
50
0.9999
Chamelcon
0,00001
0.005
50
0.99999
0.001
0.001
0.9999
0.001
0.05
0.9999
Actor
0.005
0.05
50
0,9999
0.001
0.05
50
0,9999
0.01
0.05
50
0. 0.9999
Squirrel
0,005
0,05
50
0.9999
0
0.05
5
0.99999
0,005
0,0001
50
0.9999
Table 12: Settings and code links of various baseline methods.
Methods
Source Code
k-Shot Sampling
task.py
Dataset Split
Protz/blob/mmm/promppp.
graphivatta/load-aaata.
Evaluation
ProC/hbob//mam//romppt..o
DGI
https://iilubbcoomPccaaviii
GRACE
GraphMAE
pyg
GPPT
https///tttubbbcm///MiigCheeeent
GraphPrompt
GPF/GPF+
https//gattub.com//uuuoooF?
All-in-one
httgs//ghttbbooommmmaattorrrrrrr https:/ggthhbbboo////ttttttggggp
EdgeePromp//EEggerrooptt
the summary of the graph. GGD (50 further explores the DGI, summarizing it group discrim-
ination task, greatly reducing the computational time overhead. MVGRL [51] introduces graph diffusion generate different scale subgraphs improve the pipeline of DGL. GRACE [29 uses InfoNCE [45 46) to optimize by maximizing the similarity of two augmented nodes generated by
the same node and minimizing the similarity of other nodes to train the model. GCA [47] improve the augmentation strategy by defining the importance of different nodes and edges to preserve semantic information. GraphCL [37 and JOAO [52] use this paradigm to global-global graph representation contrast. AD-GCL [53] has similar idea, which designs a Icarnable augmentation strategy, and trains the model by maximizing mutual information between node representation and augmented graph. Some works [54]
[9 60 61 focus on design effective sampling strategy, and other works [62 63 further introduce additional knowledge into GCL. AFGRL [64].
35 56
57
58
SimGRACE [65], NeCo [66] and AFGCL 66 propose augmentation free paradigm to optimize sampling. PolyGCL [68] and S3GCL 669 focus on designing polynomials with learnable filters to generate different spectral contrastive views. GraphMAE [30], as the method of masked graph autoencoders, utilizes randomly mask mechanism from the input with the graph autoencoder [70] architecture to reconstruct the node features or structures. GraphMAE2 [71] proposes multiview
31

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random re-mask decoding, the node representations are randomly re-masked multiple times, to introduce randomness into feature reconstruction. BGRL [3] adopts BYOL [72], which trains the online encoder by predicting the target encoder to generate efficient node representations. This backbone is followed by some recent works, such as SGRI. 73 and SGCL [32].
Graph Prompt Learning, Graph prompt learning aims to address the gap between pretrained models and downstream tasks by introducing tunable components into the inputs, model parameters, or outputs of pretrained models. This approach facilitates the alignment of the pretraining domain with the target domain, thereby improving performance in downstream tasks, particularly in few-shot fine-tuning scenarios. GPPT [6] introduces structure tokens and task tokens, transforming the node classification task into a form consistent with link prediction. GPF/GPF-plus M7. from the perspective of the feature space, inject global and specific prompt vectors into nodes, bringing the prompt-tuning paradigm into graph representation learning. SUPT 74 extends GPF-plus to the subgraph level, modifying the attention mechanism to a GCN-like aggregation that incorporates neighborhood in- formation. IA-GPI.
75 further advanced this by introducing an instance-awar mechanism that maps node representations to a prompt space, and quantizes them into a codebook using Vector Quantization (VQ) (76). The resulting quantized prompts are then combined with node features as input for the pre-trained model. RELIEF [77] employs reinforcement learning to select small, efficient set of nodes for prompt generation, creating node-specific prompts to avoid the potential interference of applying prompts to all nodes. All-in-one [14] unifies the "pretraining -rongppppppp""" paradigm by converting node and edge tasks into graph-level tasks by building an induced subgraph. It then adds Icarnable prompts to the node features in a weighted manner to construct prompt graph. By combining meta-learning with this process, the prompts can adapt to multiple downstream tasks. HeterGP 78 extends this paradigm by considering heterophilic scenarios. GraphPrompt [24 unifies node-level and graph-Ievel tasks into subgraph similarity computation and incorporates a learnable prompt vector into the readout layer of GNNs, enabling the model adapt flexibly to various downstream tasks. Building on this, GraphPrompt+ [15) generalizes pre-training tasks to arbitrary contrastive learning tasks and introduces prompt vectors into each GNN layer, thereby leveraging hierarchical knowledge. MultiGPrompt [26] further adopts multi-task pre-training to learn more comprehensive and multi-level representations, PRODIGY (79 introduces task graphs to unify pretraining and downstream tasks via in in-context learning. It avoids parameter tuning by reformulating tasks as link prediction between data and label tokens. GraphControl [2 aligns cross-domain graphs via conditional prompts inspired by ControlNet [80] enabling semantic consistency in transfer learning. Moreover, some works are designed for considering heterogeneous graphs. HGPrompt [8], based on GraphPrompt, decomposes heterogeneous graph into multiple homogeneous subgraphs and introduces feature prompts and heterogeneity prompts, thereby proposing the prompt framework applicable to both homogeneous and heterogeneous graphs. HetGPT [82] further introduces virtual class prompts and heterogeneous feature prompts, and adopts a multi-view neighborhood aggregation mechanism to effcetively model the complexity of heterogencous neighborhood structures, construct- ing a general framework for heterogeneous graph prompting. HiGPT [83] targets more complex and dynamic heterogencous graph scenarios by employing muntext-arrmmttrrizze heterogeneity projectors and L.LMs to generate node embeddings, while leveraging instruction tuning in downstream tasks to enhance generalization ability. Although various graph prompt strategies have advanced the field, there remains no unifed understanding of how these prompts interact with pretrained models, which is the problem our work tries to explain and solve.
Prompt Techniques in Graph Foundation Models. Due to its simple and efficient design, prompt techniques is widely used in some graph foundation models and LLM+GNN paradigms. GCOPE [38 proposes the concept of a "Coordinator", which introduces a set of virtual nodes to bridge different datasets, enabling the model to learn knowledge across multiple domains and transfer it to wide range of downstream tasks. OFA [16] and GOFA [84] introduce LLMs into graph learning. Specifically, OFA (16) first transforms all graph data into text-attributed graphs (TAGs), and augments the NOI (node of interest) subgraph with NOI prompt nodes and class prompt nodes. The data are then processed sequentially by LLM and GNN to predict the category of the NOI. GOFA (84 interleaves GNN layers with LLM layers, which not only preserves the ability to learn graph structures but also equips the model with text generation capabilities, thereby enabling broader generalization to downstream tasks. GraphPrompter [85] projects TAGs into a semantic space to align them. A GNN then encodes the graph structure, and the resulting node embeddings are concatenated into prefix tokens. These tokens are prepended to the input text, enabling frozen LLM to understand and reason about the graph data. ZeroG [12] uses LoRA 88 to fine-tune pretrained Language Model (LM).
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It creates a neighborhood-awwln prompt that aggregates local topological information via message passing, helping the LM generate effective representations for zero-shot tasks. UniGraph [87] maps cross-domain graph features into a unified LLM semantic space. It trains cascaded LM-GNN encoder using masked language modeling task. For downstream tasks, uses in-context learning for
few-shot transfer and combines LoRA with fine-tuning for zero-shot transfer. LLaGA [8] transforms graph structures into node sequences using either a neighbor-
or hop-count based approach. The resulting sequences, which are rich in structural information, are combined with prompt tokens and fed into frozen LLM for various downstream tasks. GraphGPT [9] uses a two-phase prompt tuning to enhance LLM understanding of graph structures. The first phase uses self-supervised prompt tuning with graph matching task to learn a projector. The second phase involves task-specific fine-tuning to customize the reasoning of LLMs for different downstream tasks. The GFT [90] framework defines computation tree as a token, and uses VQ to maintain a token vocabulary during pretraining- For downstream prompting, any task be re-framed computation tree tokens. GIT [91] adopts similar concepts to GFT, but adds theoretical proofs to demonstrate the stability, transferabilityy, and generalization of the task tree.
the classification of these
D Limitations
Despite the excellent results achieved by our proposed UniPrompt method, several limitations should be considered:
Limited Integration with LLMs: Our proposed method currently focuses adapting traditional graph pretrained models and does not explore the integration of Large Language Models (LLMs) encoders. This is notable limitation, given the increasing prominence of LLMs in generating powerful, semantically rich node representations from textual attributes. The full potential of UniPrompt in semantically driven graph tasks and its applicability to emerging paradigms like zero-shot graph learning remain unexplored.
Hyperparameter Dependency: The effectiveness of UniPrompt hinges on two key hy- perparameters: the temperature coefficient, т, which balances the original and prompt topologies, and the number of neighbors, k, in the ANN graph. The hyperparameter analysis reveals that the optimal settings for these parameters vary significantly across datasets of different types (e.g.. homophilic vs. heterophilic) and scales. This necessitates careful tuning when applying the method to new datasets, which adds to its practical complexity.
The current evaluation is exclusively focused node classifica- tion, particularly in few-shot settings. Whether UniPrompt can be effectively generalized other important graph learning tasks, such graph classification, link prediction, or community detection, remains unverified. These tasks have different requirements for global structural information or edge-level relationships, for which the current node centric topological prompt may not be optimal.
Limited Task Coverage:
E Broader Impacts
The introduction of UniPrompt represents a significant advancement in the field of graph prompt learning. The broader impact of this work includes:
By theoretically dissecting the underlying mechanisms of different prompting strategies, this research proposes clearer design paradigm: graph prompt learning should focus on unleashing the capability of pretrained models, and the classifier adapts to downstream scenarios. This perspective offers theoretical guidance for building more powerful and versatile Graph Foundation Models (GFMs), helping to steer the field toward a more unified and efficient direction.
Promoting the Development of Graph Foundation Models:
Further Integration with LLMs:
The principles of UniPrompt can be powerfully com- bined with Large Language Models (LLMs), which have become de facto foundation models for language. this paradigm, an LLM could serve as a powerful feature encoder for text- attributed graphs, and UniPrompt 's adaptive topology would then refine the graph structure to best suit the rich semantic representations provided by the L.M. This integration would test the universality of our approach, demonstrating how a learnable, input-level prompt can
33

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effectively guide a powerful, but non con-graph-natee native, pre-trained model to reason over graph
structures.
Extension to Multiple Tasks: While the current work focuses on node classification, the core idea of learning an adaptive topology task-agnostic and holds significant promise for other fundamental graph tasks. For graph classification, the learned prompt graph could highlight key subgraphs or motifs crucial for determining graph's overall label. For link prediction, the adaptive topology could help the model capture higher-order structural patterns predictive of missing edges. Extending UniPrompt to these diverse tasks would be crucial step in validating its effectiveness as more universal adaptation method for graph models.
Enhancing Model Robustness and Generalization: The method enhances the gener- alization capability of pre-trained models by learning a topology that adaptive to the downstream task, especially when handling distribution shifts (e.g., from homophily to het- erophily). This concept inspire further research into improving cross-domain adaptation and Out-of- Disttiibbuuttn (OOD) generalization, which is vital for building reliable and stable AI systems that can operate diverse, real-world environments.
34


Reviews:
[{"review_id": "2Ot5nzotJf", "reviewer": "Reviewer_5BCP", "summary": "The 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.", "questions": "As previously discussed.", "limitations": "As previously discussed.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "strengths:\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.", "was_revised": false, "final_justification": "", "rebuttal": "We thank you for your support of our work. We appreciate your insights and have addressed your comments below.\n\n### **To Weakness 1:**\n\nWe have added one graph prompt learning baseline All-in-one [1]. Regarding the 1-shot experiment, as shown in the table below:\n\n| All-in-one(1-shot)  | Cora       | CiteSeer   | PubMed     | Cornell     | Texas       | Wisconsin   | Chameleon  | Actor      | Squirrel   |\n| ------------------- | ---------- | ---------- | ---------- | ----------- | ----------- | ----------- | ---------- | ---------- | ---------- |\n| DGI-pretrained      | 32.10±6.50 | 28.77±3.12 | 35.87±7.53 | 26.67±12.42 | 31.53±13.14 | 24.82±8.77  | 22.41±3.58 | 19.93±5.23 | 21.61±5.87 |\n| GRACE-pretrained    | 34.53±5.86 | 24.06±6.18 | 34.51±7.45 | 22.17±5.40  | 27.37±13.79 | 36.17±6.32  | 19.46±0.29 | 19.04±4.30 | 22.03±2.46 |\n| GraphMAE-pretrained | 28.96±4.87 | 31.72±2.78 | 39.99±6.21 | 22.33±6.43  | 29.71±20.15 | 29.85±13.99 | 20.13±1.81 | 21.08±2.17 | 20.39±0.93 |\n\nWe will include these experimental results in the future version.\n\n### **To Weakness 2:**\n\nWe thank you for pointing out the shortcomings in our work. We acknowledge that we did not discuss the limitations section thoroughly enough in the initial draft. Here, we add the three types of experiments you mentioned:\n\nRegarding our computational overhead, we provide the 1-shot time and space table for various baselines of DGI-pretrained models, are shown in the table below:\n\n| Methods \\| Time (s/per epoch) | Cora   | CiteSeer | PubMed | Cornell | Texas  | Wisconsin | Chameleon | Actor  | Squirrel |\n| ------------------------------------------- | ------ | -------- | ------ | ------- | ------ | --------- | --------- | ------ | -------- |\n| GPPT                                        | 0.0118 | 0.0117   | 0.0130 | 0.0123  | 0.0120 | 0.0119    | 0.0109    | 0.0051 | 0.0127   |\n| GraphPrompt                                 | 0.0035 | 0.0008   | 0.0101 | 0.0242  | 0.0037 | 0.0136    | 0.0003    | 0.0006 | 0.0024   |\n| GPF                                         | 0.0021 | 0.0031   | 0.0031 | 0.0020  | 0.0021 | 0.0020    | 0.0031    | 0.0022 | 0.0042   |\n| GPF+                                        | 0.0033 | 0.0032   | 0.0033 | 0.0021  | 0.0023 | 0.0021    | 0.0032    | 0.0022 | 0.0042   |\n| EdgePrompt                                  | 0.0018 | 0.0027   | 0.0042 | 0.0025  | 0.0017 | 0.0017    | 0.0041    | 0.0031 | 0.0127   |\n| EdgePrompt+                                 | 0.0024 | 0.0027   | 0.0042 | 0.0025  | 0.0024 | 0.0024    | 0.0040    | 0.0035 | 0.0122   |\n| Ours                                        | 0.0054 | 0.0052   | 0.0039 | 0.0040  | 0.0045 | 0.0039    | 0.0047    | 0.0068 | 0.0073   |\n\n| Methods \\| Space (MB) | Cora  | CiteSeer | PubMed | Cornell | Texas | Wisconsin | Chameleon | Actor  | Squirrel |\n| ----------------------------------- | ----- | -------- | ------ | ------- | ----- | --------- | --------- | ------ | -------- |\n| GPPT                                | 80.6  | 153.1    | 311.7  | 26.6    | 26.7  | 28.5      | 118.4     | 155.1  | 365.9    |\n| GraphPrompt                         | 642.6 | 896.1    | 2366.2 | 31.5    | 31.8  | 37.0      | 1170.4    | 913.7  | 3597.7   |\n| GPF                                 | 119.8 | 267.4    | 470.1  | 29.9    | 29.9  | 32.1      | 193.4     | 236.8  | 662.4    |\n| GPF+                                | 130.0 | 361.8    | 470.5  | 32.3    | 32.3  | 34.9      | 194.3     | 236.9  | 662.6    |\n| EdgePrompt                          | 191.1 | 398.0    | 746.2  | 32.6    | 31.9  | 36.3      | 550.8     | 379.8  | 2608.3   |\n| EdgePrompt+                         | 191.5 | 398.3    | 746.2  | 33.2    | 32.0  | 36.7      | 551.0     | 380.8  | 2608.4   |\n| Ours                                | 511.5 | 674.5    | 566.2  | 55.1    | 55.2  | 67.7      | 539.9     | 1392.6 | 1603.9   |\n\nBy analyzing the two tables, we observe that our method achieves comparable computational costs compared to various baselines across all datasets. Both the training time per epoch and the GPU memory usage of our approach are reasonable and manageable in practice. These Time and GPU usage tables will be included in future versions.\n\nRegarding the scalability issue, we ran experiments on the large-scale heterophilic dataset arxiv-paper (169,343 nodes, 1,166,243 edges). Here, we use a simplified kNN by randomly sampling 1000 nodes, then connecting each node to its top-k most similar sampled nodes. We test three pretrain strategies under 5-shot setting, comparing with fine-tuning. The accuracy and computational cost are shown in the table below.\n\n| 5-shot               | Arxiv-Year (acc) | Preprocessing Time (s) | Training Time (s/per_epoch) |\n| -------------------- | ---------------- | :--------------------- | --------------------------- |\n| Fine-tune (DGI)      | 28.27±5.99       | -                      | 0.0138                      |\n| Ours (DGI)           | 32.48±6.37       | 1.25                   | 0.0224                      |\n| Fine-tune (GRACE)    | 24.60±1.04       | -                      | 0.0205                      |\n| Ours (GRACE)         | 25.17±2.83       | 1.26                   | 0.0320                      |\n| Fine-tune (GraphMAE) | 23.24±1.58       | -                      | 0.0427                      |\n| Ours (GraphMAE)      | 24.25±5.43       | 1.32                   | 0.0618                      |\n\nAs shown in the table, our method incurs minimal preprocessing time and only a slight increase in training time per epoch, with small epoch counts (typically less than 500). This demonstrates that our approach is scalable to large graphs. This table will be included in the future versions.\n\nRegarding the noise robustness issue, we performed Gaussian noise perturbations of different scales on the original features, 1-shot results as shown in the table below:\n\n| 1-shot DGI-pretrained | Cora        | CiteSeer    | PubMed     | Cornell     | Texas       | Wisconsin   | Chameleon  | Actor      | Squirrel   |\n| --------------------- | ----------- | ----------- | ---------- | ----------- | ----------- | ----------- | ---------- | ---------- | ---------- |\n| Noisy-0.01            | 44.42±10.49 | 32.39±12.85 | 61.00±5.31 | 50.62±14.07 | 46.72±12.88 | 62.51±10.50 | 20.80±2.43 | 26.96±4.35 | 22.50±2.07 |\n| Noisy-0.05            | 23.23±9.21  | 19.77±1.66  | 40.01±0.98 | 49.22±12.02 | 38.75±13.32 | 61.03±9.79  | 20.72±0.76 | 24.67±2.96 | 20.41±0.74 |\n| Noisy-0.2             | 27.82±5.26  | 15.73±5.90  | 39.46±0.37 | 28.59±5.96  | 33.91±10.64 | 42.86±16.34 | 20.08±2.77 | 22.15±2.70 | 20.21±0.27 |\n\nWe observe that at a noise level of 0.01, some datasets (e.g. PubMed, Wisconsin and Actor) maintain their accuracy, which can be attributed to a minor noise-induced augmentation effect. However, a noise level of 0.05 begins to noticeably impact model performance. At a substantial noise level of 0.20, the accuracy across most datasets experiences a significant decline. This analysis will be incorporated into our future version.\n\nFrom the results in the tables, we conclude that our computational overhead for different datasets is acceptable in practical applications. Moreover, for large datasets, our performance is satisfactory, and the overhead remains within acceptable limits. Regarding the noise issue, from a method perspective, our approach is based on generating prompt topologies from features. Therefore, it encounters problems with noisy features, as this disrupts the completeness of the features. When both features and topology are misaligned with the pre-trained model, our method faces challenges in solving the problem, and our improvement is less ideal. We will include this discussion in the limitations section and present it in future versions. Thank you again for your support of our work.\n\n[1]. Sun, et al. All in One: Multi-Task Prompting for Graph Neural Networks, KDD 2023."}, {"review_id": "nsJV7G2jXn", "reviewer": "Reviewer_Gjzk", "summary": "This 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.", "questions": "1. 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?", "limitations": "N/A", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "Read the author response, the response addresses my concerns, so I decide to raise the score from 3 to 4.", "rebuttal": "Thank you for your constructive feedback; we appreciate your insights and have addressed your comments below.\n\n### **To Weakness 1:**\n\nThank you for pointing out this potential misunderstanding. We agree $p_{\\psi}$ needs clarification. We clarify that, in current practice, $p_{\\psi}$ typically consists of two components: a prompt function and a classifier. Many existing works follow this paradigm, focusing on separately optimizing these two modules. However, our analysis in Section 4 aims to demonstrate that the combination of a prompt and a classifier is equivalent to a single classifier. This suggests that such methods may not fully exploit the advantages of prompting. To avoid confusion, we will revise Figure 1 in the future version to more clearly illustrate these two components.\n\n### **To Weakness 2 \\& Question 1:**\n\nThank you for your careful review. Indeed, input-level, layer-wise and representaion-level, these three forms represent different scenarios of the three functions. Our formulas are simplified in the Preliminary, which may lead to some confusion. We will provide more detailed formulas here to facilitate better understanding. The optimization objective for all graph prompt learning can be expressed as:\n\n$$\n\\max\\_{\\Psi} \\frac{1}{|\\mathcal{D}|} \\sum\\_{(\\mathbf{A}, \\mathbf{X}, y) \\in \\mathcal{D}} \\sum\\_{i=1}^{N} \\log p\\left(y\\_{i} \\mid \\text{Predict}\\_{\\Psi}\\left(\\mathbf{A}, \\mathbf{X}, v_{i}; f\\_{\\theta}\\right)\\right)\n$$\n\nwhere $\\mathcal{D} = \\{ ( \\mathbf{A}, \\mathbf{X}, y )\\}$ is downstream dataset, $\\Psi$ represents all trainable prompt parameters, $f_{\\theta}$ is the frozen pretrained encoder. $ \\text{Predict}\\_{\\Psi}(\\cdot) $ is a unified prediction function that takes the input graph $(\\mathbf{A}, \\mathbf{X})$, node $v_i$, the pretrained encoder $f_{\\theta}$, to produce the final prediction for node $v_{i}$.\n\nFor Input-level, $\\Psi$ acts on the input $(\\mathbf{A}, \\mathbf{X})$, transforming it before it enters $f_{\\theta}$. For Layer-wise, $\\Psi$ is embedded within $f_{\\theta}$'s layers. For Representation-level, $\\Psi$ operates on the representations from $f_{\\theta}$ (often as part of the classifier), directly influencing the classification.\n\n### **To Weakness 3 & Question 2:**\n\nWe appreciate the reviewer gives us the opportunity to clarify. We categorize existing graph prompt methods into three main types: input-level, layer-wise, and representation-level. We categorize these methods based on their mechanisms. We want to analyze these various approaches to gain a deeper understanding of the underlying prompt mechanism.\n\nRegarding representation-level methods, our analysis demonstrates that they are equivalent to a classifier. This suggests that their mechanism contradicts the core advantages of prompting and fails to leverage the benefits of prompts, as similar results can be achieved with a classifier alone. As for layer-wise methods, due to their inherent design complexity and strong reliance on the internal representations of pre-trained models, they are not suitable for black-box pre-trained GNNs. Therefore, we do not consider these methods in our work. \n\nIn contrast, input-level methods avoid the limitations and preserve the core advantages of prompting without requiring access to model internals. Thus, they are the most promising among the three categories for our setting. We thus design an input-level prompt mechanism. Through this process, we get our core perspective: \"Graph prompt learning should focus on unleashing the capability of pretrained models, and the classifier adapts to downstream scenarios.\" This viewpoint clearly defines the distinct roles and mechanisms of the two crucial downstream components: the prompt and the classifier.\n\n### **To Question 3:**\n\nWe appreciate your thoughtful consideration of our experiments. The Cornell, Texas, and Wisconsin datasets are relatively sparse, with a limited number of edges. When our kNN-generated prompt adds a number of homogeneous edges exceeding the original edge count, the nature of these datasets is substantially changed. This process might even shift the inherent property of data from heterophily towards homophily, in turn, allows the model to perform well on these datasets. Conversely, Chameleon, Actor, and Squirrel are comparatively denser datasets. Adding a specific number of edges to these datasets primarily introduces additional similar information to the nodes. It doesn't really change the dataset's fundamental nature. That's why, even though our model improves performance on these three datasets, the boost is much higher for Cornell, Texas, and Wisconsin.\n\n### **To Question 4:**\n\nWe appreciate the reviewer insightful comment. As shown in Figure 4, the performance varies with $\\tau$: smaller values tend to perform better on heterophilic graphs, while larger values suit homophilic ones. This aligns with our understanding that adding homophilic bias helps enhance structural learning on heterophilic graphs.\n\nWhile $\\tau$ requires tuning, we view it as part of the prompt design process. In practice, prior knowledge of graph homophily can guide the selection of $\\tau$, reducing the search space. Additionally, future work could explore learnable or adaptive $\\tau$ mechanisms, which may remove the need for manual tuning altogether. We thank the reviewer for raising this valuable direction."}, {"review_id": "uO3WUycRh9", "reviewer": "Reviewer_5pxd", "summary": "This 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.", "questions": "The 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?", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths:\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", "was_revised": true, "final_justification": "It has addressed my concerns, and I will keep my score.", "rebuttal": "Thank you for your support of our work! We appreciate your insights and have addressed your comments below.\n\n### **To Weakness 1:**\n\nThank you for pointing out this potential misunderstanding. The notation $\\mathcal{G}$ in this figure is indeed unclear. \n\nAs we stated in the preliminary section, under the general \"pretrain, fine-tune\" paradigm of graph representation learning, $ \\mathcal{G} = (\\mathbf{A}, \\mathbf{X})$, which means it includes both feature and topological elements. Here, we generalize that input-level prompts can arbitrarily affect both components. We will revise Figure 1 to provide a more detailed and clear representation of this concept in the future version.\n\n### **To Weakness 2:**\n\nThank you for your valuable suggestions. We completely agree that the introduction to layer-wise prompt methods was insufficient in the initial draft. As you pointed out, while such methods have been less explored in the graph domain, they have already made significant impacts in fields such as vision and multimodal learning. We will include discussions of works from other domains such as VPT [J1] and MaPLe [C1] in future versions, and add detailed analyses between layer-wise prompts in graph domain versus vision/multimodal domains, including design methods, applicable scenarios, and performance differences across different data domains. we will include these discussion in the future version.\n\n### **To Weakness 3:**\n\nThank you for your suggestions. We provide the fully experiment of Fine-tune and Linear-probe here, as shown in the table below:\n\n| Method                     | Cora       | CiteSeer   | PubMed     | Cornell    | Texas       | Wisconsin   | Chameleon  | Actor      | Squirrel   |\n| -------------------------- | ---------- | ---------- | ---------- | ---------- | ----------- | ----------- | ---------- | ---------- | ---------- |\n| Fine_tuning (1-shot DGI)   | 50.22±9.28 | 42.58±8.87 | 53.90±8.30 | 35.23±8.84 | 37.50±13.57 | 33.91±10.56 | 24.42±3.19 | 21.36±3.28 | 22.27±4.10 |\n| Linear_probe  (1-shot DGI) | 49.77±9.74 | 43.16±7.60 | 55.76±9.43 | 34.26±8.60 | 36.21±13.77 | 28.71±9.38  | 23.64±2.17 | 21.33±2.62 | 22.82±4.10 |\n| Fine_tuning (1-shot GRACE)  | 48.59±9.20 | 46.16±6.30 | 57.97±7.55 | 34.18±10.18 | 31.52±13.08 | 32.23±8.96 | 26.22±2.73 | 20.81±2.86 | 21.16±2.57 |\n| Linear_probe (1-shot GRACE) | 46.22±7.92 | 46.10±6.32 | 57.87±7.60 | 34.92±9.74  | 34.84±15.65 | 31.66±8.18 | 24.27±3.84 | 20.53±3.11 | 20.81±1.82 |\n| Fine_tuning (1-shot GraphMAE)  | 45.92±9.67  | 36.47±8.35 | 54.29±9.52 | 35.82±11.30 | 37.07±14.08 | 33.54±10.16 | 22.08±3.19 | 20.85±1.68 | 21.32±2.65 |\n| Linear_probe (1-shot GraphMAE) | 50.13±12.06 | 48.08±6.96 | 58.61±8.34 | 32.27±11.28 | 38.32±13.61 | 28.40±8.67  | 23.02±2.08 | 20.56±2.91 | 21.05±1.87 |\n| Fine_tuning (3-shot DGI) | 65.09±5.73 | 60.32±4.05 | 64.81±6.80 | 41.84±6.52 | 38.75±9.34 | 41.34±7.57 | 28.66±3.99 | 22.61±2.36 | 23.02±3.54 |\n| Linear_probe (3-shot DGI) | 67.48±4.65 | 60.91±4.23 | 65.92±5.53 | 40.39±8.35 | 39.30±7.67 | 38.29±9.18 | 27.17±4.04 | 21.66±2.31 | 22.56±2.29 |\n| Fine_tuning (3-shot GRACE) | 63.99±5.69 | 60.48±4.52 | 62.03±6.10 | 42.42±7.46 | 39.73±6.45 | 40.34±5.37 | 29.73±4.02 | 21.70±2.14 | 23.77±2.23 |\n| Linear_probe (3-shot GRACE) | 63.68±5.99 | 60.35±3.99 | 65.71±5.87 | 41.60±7.19 | 39.53±6.62 | 41.46±5.28 | 29.02±4.60 | 21.55±1.38 | 21.62±3.47 |\n| Fine_tuning (3-shot GraphMAE) | 66.38±6.34 | 58.57±5.82 | 62.51±4.55 | 46.09±8.50 | 43.91±8.88 | 48.31±5.70 | 27.33±3.17 | 21.40±1.56 | 21.18±1.20 |\n| Linear_probe (3-shot GraphMAE) | 70.74±4.52 | 60.60±4.96 | 66.90±4.70 | 38.52±7.65 | 43.13±8.47 | 41.40±5.54 | 29.02±4.05 | 22.08±1.77 | 21.91±1.74 |\n| Fine_tuning (5-shot DGI) | 73.01±2.55 | 65.08±3.52 | 70.91±4.65 | 45.78±5.65 | 43.20±9.51 | 43.26±7.43 | 28.81±2.82 | 23.65±2.38 | 22.58±2.75 |\n| Linear_probe (5-shot DGI) | 72.39±2.01 | 65.11±2.62 | 70.32±4.19 | 45.23±6.87 | 42.81±8.09 | 41.66±5.68 | 28.80±2.67 | 22.55±2.40 | 23.53±1.70 |\n| Fine_tuning (5-shot GRACE) | 70.49±2.28 | 64.19±3.49 | 70.42±5.36 | 47.15±6.77 | 43.09±8.74 | 42.51±5.92 | 34.00±2.48 | 22.61±1.91 | 25.22±1.65 |\n| Linear_probe (5-shot GRACE) | 71.09±2.18 | 63.65±3.29 | 71.34±6.46 | 47.07±6.66 | 42.11±8.02 | 41.91±6.20 | 32.78±3.15 | 22.23±1.88 | 24.05±1.58 |\n| Fine_tuning (5-shot GraphMAE) | 73.85±2.87 | 64.59±4.32 | 72.83±3.21 | 58.24±4.44 | 47.62±6.96 | 50.29±6.59 | 28.78±2.47 | 21.22±4.17 | 22.38±1.12 |\n| Linear_probe (5-shot GraphMAE) | 75.78±2.38 | 66.17±2.72 | 70.08±4.82 | 43.71±5.71 | 45.00±7.98 | 41.11±7.54 | 31.31±3.63 | 22.51±2.23 | 22.25±1.75 |\n\nFrom the table, we observe that the performance of Linear-probe is comparable to Fine-tuning across most datasets. Notably, on homophilous datasets (e.g., Cornell, Wisconsin, and Texas), Linear-probe often outperforms Fine-tuning. In contrast, Fine-tuning tends to perform better on some heterophilous datasets.\n\nComparing this table with Table 1 in the paper, we find that Linear-probe achieves competitive or even superior performance compared to multiple baselines, particularly for representation-level baselines (GPPT and GraphPrompt). The improvement is more pronounced on heterophilous datasets such as Cornell, Wisconsin, and Texas, while there are also slight gains on Chameleon, Actor, and Squirrel. We will include these results in the future version.\n\n### **To Question:**\n\nThank you for your question. We have discussed part of the potential scenarios in section 5, and we think that overfitting is a common issue in few-shot downstream settings. We attribute this to the use of inappropriate prompt mechanisms, which obscure the prompt's true function, leading to unstable convergence and even negative optimization during training. This phenomenon was part of our motivating experiments.\n\nTo address this, first, we designed our prompt using kNN to provide the model with a stable structural prior. This transforms the prompt from discrete, random vectors into a stable structure built upon the semantic relationships between nodes. Second, we employed bootstrapping to further mitigate overfitting, ensuring that both the original topology and the prompt's topology are utilized effectively.\n\nFinally, our approach clarifies the roles of the two key downstream modules: the prompt and the classifier, allowing both to contribute effectively. This not only reduces the risk of overfitting but also ensures that the structural prompt smoothes the optimization landscape, leading to a more stable training process and alleviating potential gradient issues like exploding or vanishing gradients."}, {"review_id": "gJxnEeKHVU", "reviewer": "Reviewer_oJWJ", "summary": "This 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.", "questions": "1.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?", "limitations": "YES", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\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).", "was_revised": true, "final_justification": "The authors have provided a detailed and satisfactory response to my concerns, so I am increasing my rating.", "rebuttal": "Thank you for your constructive feedback; we appreciate your insights and have addressed your comments below.\n### **To W1 & Q2:**\n\nWe thank the reviewer for the thoughtful comments.\n\nWhile techniques like kNN and bootstrapping have been used in GSL and SSL, our work is different in how we apply them to graph prompt learning, with the goal of unleashing the capabilities of pretrained GNN models. The kNN algorithm is consistent with the homophily assumption underlying widely adopted pre-trained GNN models. By constructing a similarity-based graph, kNN enables the input data to better align with these models, allowing information from similar nodes to be aggregated, thereby enabling more effective adaptation. This design aligns well with our core perspective: graph prompt learning should focus on unleashing the capability of pretrained models. Compared to feature-based or connection-based prompt injection methods, kNN provides a clear and structure-based method that help the pretrained GNN models work more effectively.\n\n- For random topology: Randomly linked graphs may lack meaningful structure, hindering the pretrained model's ability to capture node similarity or semantic relationships. This severely impacts the performance of downstream tasks, with an average accuracy drop of over 25%. Detailed experiments are provided in the response to weakness 3.\n\n- For learnable topology: While the idea of end-to-end learnable topology is appealing, it typically requires complex design and greater computational overhead to ensure the effectiveness of the learned structure. We agree that this represents a promising direction for future research, and we thank you for highlighting this point.\n\n\n### **To W2:**\n\nWe categorize existing graph prompt methods into three main types according to their mechanisms: input-level, layer-wise, and representation-level. We want to analyze these various approaches to gain a deeper understanding of the underlying prompt mechanism.\n\nRegarding representation-level methods, our analysis demonstrates that they are equivalent to a classifier. This suggests that their mechanism contradicts the core advantages of prompting and fails to leverage the benefits of prompts, as similar results can be achieved with a classifier alone. As for layer-wise methods, due to their inherent design complexity and strong reliance on the internal representations of pre-trained models, they are not suitable for black-box pre-trained GNNs. Therefore, we do not consider these methods in our work.\n\nIn contrast, input-level methods avoid the limitations and preserve the core advantages of prompting without requiring access to model internals. Thus, they are the most promising among the three categories for our setting. We thus design an input-level prompt mechanism. Through this process, we get our core perspective: \"Graph prompt learning should focus on unleashing the capability of pretrained models, and the classifier adapts to downstream scenarios.\", which clearly defines the distinct roles and mechanisms of the two crucial downstream components: the prompt and the classifier.\n\n### **To W3:**\n\nWe appreciate this suggestion. Since both components are essential and cannot be simply removed, we conduct replacement experiments: (1) replacing kNN with random topology, (2) replacing bootstrapp with simple addition of original and prompt graph, (3) discarding original graph totally. 1-shot results as shown in the table below:\n\n| DGI-pretrained | Cora       | CiteSeer   | PubMed     | Cornell     | Texas       | Wisconsin  | Chameleon  | Actor      | Squirrel   |\n| --------------------- | ---------- | ---------- | ---------- | ----------- | ----------- | ---------- | ---------- | ---------- | ---------- |\n| Random_Topo           | 44.06±8.80 | 36.69±9.18 | 52.85±4.00 | 27.81±11.11 | 18.59±3.78  | 35.43±4.06 | 20.79±2.27 | 20.73±2.32 | 22.33±1.58 |\n| Simple_Add          | 24.54±4.95 | 26.27±3.37 | 43.80±9.69 | 51.72±16.49 | 38.28±13.17 | 55.89±4.91 | 23.34±3.90 | 23.47±2.09 | 23.56±1.20 |\n| Discard_Topo          | 28.17±6.44 | 30.15±6.88 | 37.33±6.77 | 52.03±16.25 | 46.72±13.46 | 52.63±8.35 | 23.20±4.03 | 24.95±3.74 | 22.31±1.34 |\n\nFrom the table, we can find that Random_Topo maintains some of effectiveness on homophilic datasets while showing reduced performance on heterophilic ones. Conversely, for Simple Addition and Discarding Topology, heterophilic datasets still retain some performance. However, performance on homophilic datasets drops significantly, as their original structure is crucial for classification. This table will be included in the revised versions.\n\n### **To W4:**\nWe appreciate you raising this point. Since our prompt topology is built on node features, it is sensitive to feature noise, which can lead to distorted graph structures. When both features and topology are misaligned with the pre-trained model, our method faces challenges in solving this problem. The 1-shot learning results under varying levels of Gaussian noise are summarized in the following table:\n\n| DGI-pretrained | Cora        | CiteSeer    | PubMed     | Cornell     | Texas       | Wisconsin   | Chameleon  | Actor      | Squirrel   |\n| --------------------- | ----------- | ----------- | ---------- | ----------- | ----------- | ----------- | ---------- | ---------- | ---------- |\n| Noisy-0.01            | 44.42±10.49 | 32.39±12.85 | 61.00±5.31 | 50.62±14.07 | 46.72±12.88 | 62.51±10.50 | 20.80±2.43 | 26.96±4.35 | 22.50±2.07 |\n| Noisy-0.05            | 23.23±9.21  | 19.77±1.66  | 40.01±0.98 | 49.22±12.02 | 38.75±13.32 | 61.03±9.79  | 20.72±0.76 | 24.67±2.96 | 20.41±0.74 |\n| Noisy-0.2             | 27.82±5.26  | 15.73±5.90  | 39.46±0.37 | 28.59±5.96  | 33.91±10.64 | 42.86±16.34 | 20.08±2.77 | 22.15±2.70 | 20.21±0.27 |\n\nWe observe that a 0.01 noise level shows minor augmentation, maintaining accuracy in some datasets (e.g. PubMed, Wisconsin and Actor). However, 0.05 noise begins to impact performance, and 0.20 noise significantly degrades accuracy across most datasets, with an average accuracy drop of over 30%. This analysis will be incorporated into future versions.\n\nFor the extreme heterophily cases, our method can address these situations. Our experiments demonstrate that our approach works effectively on different heterophilic datasets. Conversely, when the alignment between upstream and downstream data is already relatively high (e.g. both training and testing use homophilic datasets), our improvement is less pronounced. This discussion will be incorporated into future versions. \n\n### **To Q1:** \n\nThank you for the suggestion. We have additionally run experiments on the large-scale heterophilic dataset Arxiv-year (169,343 nodes and 1,166,243 edges) as a supplement. Here, we use a simplified kNN by randomly sampling 1000 nodes, then connecting each node to its top-k most similar sampled nodes. We test three pretrain strategies under 5-shot setting, comparing with fine-tuning. The accuracy and computational cost are shown in the table below.\n\n| 5-shot               | Arxiv-Year (acc) | Preprocessing Time (s) | Training Time (s/per_epoch) |\n| -------------------- | ---------------- | :--------------------- | --------------------------- |\n| Fine-tune (DGI)      | 28.27±5.99       | -                      | 0.0138                      |\n| Ours (DGI)           | 32.48±6.37       | 1.25                   | 0.0224                      |\n| Fine-tune (GRACE)    | 24.60±1.04       | -                      | 0.0205                      |\n| Ours (GRACE)         | 25.17±2.83       | 1.26                   | 0.0320                      |\n| Fine-tune (GraphMAE) | 23.24±1.58       | -                      | 0.0427                      |\n| Ours (GraphMAE)      | 24.25±5.43       | 1.32                   | 0.0618                      |\n\nAs shown in the table, our method incurs minimal preprocessing time and only a slight increase in training time per epoch, with small epoch counts (typically less than 500). This demonstrates that our approach is scalable to large graphs. This table will be included in the future versions.\n\n### **To Q3:**\n\nThank you for your thoughtful consideration. Our method is designed to be agnostic to the choice of pretrained GNN. To demonstrate this, we evaluate UniPrompt on diverse pretrained backbones, including DGI, GRACE, GraphMAE and the cross-domain model FUG. The consistent performance across these settings supports the generality of our approach.\n\nThat said, we acknowledge a limitation: most pretrained models we use are trained on homophilic datasets, and our similarity-based prompting mechanism naturally aligns with such inductive biases. If pretrained GNN models or foundation models were trained exclusively on heterophilic graphs, the effectiveness of our current strategy may vary. However, this does not conflict with current practice, nearly all pretrained GNN models and foundation models are trained on homophilic benchmarks. We intend to adapt our work to heterophilic settings as future work.\n\n### **To Q4:**\nThank you for your question, we provide hyperparameter analysis to $\\tau$ and $k$ in Figure 4. In general, smaller $\\tau$ perform better on heterophilic graphs while larger $\\tau$ work better on homophilic graphs. This is consistent with the characteristics of real-world datasets, where adding more homophilic information to heterophilic datasets can further leverage topological information to improve performance. A similar principle applies to $k$, smaller $k$ works well when pretrained models are trained on homophilic graphs, and larger $k$ can enhance the intervention of the prompt graph in heterophilic graphs.\n\nWhile these hyperparameters do influence performance, they are not prohibitively sensitive. In practice, prior knowledge of graph homophily can guide the selection of $\\tau$ and $k$, reducing the tuning burden. Future work will explore learnable or adaptive $\\tau$ and $k$ to automate the process further."}]

Author rebuttal:

Meta-review: Summary:
This paper explores the challenge of prompt learning on graphs, identifying two key limitations: a lack of consensus on the underlying mechanisms and limited adaptability to different scenarios. The authors address these issues by first offering a theoretical analysis, which reveals that representation-level prompts essentially function as fine-tuning a simple downstream classifier. Based on this finding, they propose UniPrompt, a method that adapts any pretrained model, effectively leveraging its capabilities while preserving the graph's structure. Experiments across several datasets demonstrate the proposed model's effectiveness.


Strengths:
1. The theoretical analysis showing that representation-level prompts are equivalent to classifier fine-tuning provides a fresh perspective on existing graph prompt learning methods.
2. UniPrompt achieves strong empirical performance, particularly on heterophilic datasets.
3. The proposed method is simple, powerful, and easy to understand.

Weaknesses:
1. Several parts of the paper lack clarity, including Figure 1 and the explanation of layer-wise prompt methods.
2. While the theoretical perspective on representation-level prompts is interesting, the core methodology seems incremental, drawing heavily from existing ideas in graph structure learning and self-supervised learning.
3. Additional experiments are needed, such as an ablation study and comparisons with more graph prompt baselines.


In summary, the paper offers a novel perspective on prompt learning on graphs. While it presents some promising ideas, a few minor issues still exist. I recommend the authors address these points in the new revision.
Decision: Accept (poster)