Title: GraphTOP: Graph Topology-Oriented Prompting for Graph Neural Networks
Venue: NeurIPS 2025
Accepted: yes
Keywords: Graph Neural Networks

Prompt Learning
Abstract: Graph Neural Networks (GNNs) have revolutionized the field of graph learning by learning expressive graph representations from massive graph data.
As a common pattern to train powerful GNNs, the "pre-training, adaptation" scheme first pre-trains GNNs over unlabeled graph data and subsequently adapts them to specific downstream tasks. In the adaptation phase, graph prompting is an effective strategy that modifies input graph data with learnable prompts while keeping pre-trained GNN models frozen. Typically, existing graph prompting studies mainly focus on *feature-oriented* methods that apply graph prompts to node features or hidden representations. However, these studies often achieve suboptimal performance, as they consistently overlook the potential of *topology-oriented* prompting, which adapts pre-trained GNNs by modifying the graph topology. In this study, we conduct a pioneering investigation of graph prompting in terms of graph topology. We propose the first **Graph** **T**opology-**O**riented **P**rompting (GraphTOP) framework to effectively adapt pre-trained GNN models for downstream tasks. More specifically, we reformulate topology-oriented prompting as an edge rewiring problem within multi-hop local subgraphs and relax it into the continuous probability space through reparameterization while ensuring tight relaxation and preserving graph sparsity. Extensive experiments on five graph datasets under four pre-training strategies demonstrate that our proposed GraphTOP outshines six baselines on multiple node classification datasets. Our code is available at https://github.com/xbfu/GraphTOP.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a graph topology-oriented prompting framework, GraphTOP, designed to adapt pre-trained graph neural networks (GNNs) to downstream tasks, with a primary focus on node classification. In contrast to conventional feature-based graph prompting approaches, GraphTOP addresses the topological properties of subgraph rewiring by formulating it as a discrete-to-continuous optimization problem. Specifically, the proposed method transforms discrete topological structures into a continuous probability space amenable to gradient-based training through a carefully designed reparameterization technique. To enhance the framework's robustness, the authors introduce two key components: (1) an entropy regularization term to ensure tight relaxation of the probabilistic formulation, and (2) a sparsity constraint to maintain computational efficiency while preserving meaningful graph structure.The experimental evaluation demonstrates GraphTOP's superiority over six baseline methods across five real-world datasets. Comprehensive ablation studies further validate the effectiveness of the proposed topological prompting mechanism and optimization strategies. The results consistently support the claim that explicitly modeling topological adaptations yields significant performance improvements compared to feature-centric prompting approaches.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n1.The methodological design of this study is rigorous, with meticulous language descriptions demonstrating high quality. For instance, Section 4.2 provides comprehensive theoretical justification for the effectiveness of topology-oriented prompting.\n\n2.Departing from conventional feature-based graph prompting frameworks, this work pioneers topology-oriented graph prompting learning, filling a critical gap in the research field and exhibiting significant originality.\n\n3.The paper features substantial mathematical derivations complemented by clear algorithmic descriptions in the experimental section. Furthermore, the appendices contain complete mathematical proofs of relevant corollaries, substantially enhancing the clarity and rigor of the work.\n\nWeaknesses\n\n1.The experimental evaluation exclusively employs real-world graph datasets, lacking validation on synthetic (artificially constructed) datasets. This omission raises questions about the model's robustness in specialized or edge-case scenarios.\n\n2.The theoretical analysis primarily relies on the CSBM model and linear GCN architectures. The absence of complementary experiments examining generalization to nonlinear scenarios remains an important open question for discussion.\n\nQUESTIONS:\n1.How does GraphTOP perform on synthetic datasets? When scaling to larger graphs (exceeding 100,000 nodes), can it maintain a reasonable balance between model effectiveness and computational efficiency?\n\nScoring impact: Providing experiments on synthetic datasets or large-scale graphs, along with optimization strategies, could potentially improve the rating.\n\n2.The proof in Section 5.2 regarding edge rewiring's effect on inter-class distance expansion relies on linear additivity assumptions. While the authors have noted potential issues under nonlinear conditions with dynamic edge weights, could supplementary theoretical or experimental analysis be provided for nonlinear GCNs (e.g., GAT)?\n\nScoring impact: Demonstrating that GraphTOP can still enhance GNN training effectiveness under nonlinear representations may lead to rating improvement.\n\n3.The selection of parameters λ₁ and λ₂ shows significant impact on results. Could more robust optimization strategies be proposed?\n\nScoring impact: The inclusion of more robust optimization approaches may warrant rating consideration.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes GraphTOP, the first graph topology-oriented prompting framework that adapts pre-trained Graph Neural Networks by learning to modify the graph’s adjacency structure rather than node features. Through edge rewiring formulated as a differentiable probability optimization problem, GraphTOP significantly improves few-shot node classification performance across multiple datasets and pre-training strategies.\n\nSTRENGTHS AND WEAKNESSES:\nPros:\n1. The design of the method corresponds to the motivation.\n2. The time cost of the method is taken into consideration, and constraints are imposed on the method to increase the practicality of the method.\n3. Extensive experiments supported the method.\n\n\nCons:\n1. Assuming that the topological challenge is reasonable enough, in many graphs (such as chemical molecules and transportation networks), the existence of edges is determined a priori, and modifying the adjacency matrix may introduce unreasonable pseudo-structures.\n2. The experimental tasks are single and all focus on node classification.\n3. Some similar methods may be considered as baselines to compare, or for moderate discussion to distinguish them, such as Graph Structure Learning methods.\n4. Regarding the experiments with different numbers of shots, some comparisons are missing, and the experiments in the appendix did not control the pre-training method to remain consistent.\n\nQUESTIONS:\n1. Given that in many graphs (e.g., chemical molecules, transportation networks) the edge structure is determined by strict domain knowledge, how does the method ensure that modifying the adjacency matrix does not introduce unreasonable or invalid pseudo-structures?\n2. How does this work compare in practice and motivation to Graph Structure Learning methods (such as SLAPS, GSL, IDGL), which also learn or adapt the graph structure\n3. In the experiments with varying numbers of shots, why were the pre-training strategies not kept consistent across comparisons?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

comments: SUMMARY:
This work focus on the graph prompt tuning topic, it points out existing methods primarily rely on manipulating node features, which often overlook the structural prompting. So the authors propose the GraphTOP prompting framework that optimizes graph topology by learning to rewire edges within local subgraphs using a Gumbel-Softmax relaxation. The method incorporates entropy and sparsity regularization to ensure structural sparsity and interpretability. To support the design, the authors provide theoretical analysis under the Contextual Stochastic Block Model (CSBM), showing that rewiring improves class separability, and conduct experiments across 5 benchmarks demonstrating consistent gains over existing prompt methods on diverse pre-training strategies.

STRENGTHS AND WEAKNESSES:
Strengths:

1. The proposed GraphTOP achieves good performance 
2. The analysis under CSBM provides justification for why graph topology optimization improves downstream task performance.
3. Code is provided.

Weakness

1. Missing related work - This work (https://arxiv.org/pdf/2503.00750) is graph prompting from edge perspective and should be somewhat related.
2. Writing could be improved - the current writing is not easy-to-follow, the paper is presented in a dense and notation-heavy manner, adding some illustrative figure could help.

QUESTIONS:
1. Is this topology-based prompt compatible with existing feature-based prompt? 
any chance they could be combined to make further improvement?

2. Is there any visualized example to illustrate a subgraph before/after re-wiring?

LIMITATIONS:
n/a
TITLE: Official Review
CLARITY: 1
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-02

comments: SUMMARY:
The paper proposes a topology-oriented prompting strategy to adapt a pre-trained GNN to a new task by modifying the input graph topology.

STRENGTHS AND WEAKNESSES:
The paper is well written and easy to follow. The general idea of leveraging graph topology in prompting is intuitively justified and original.


I think the paper has two main weaknesses:
1. It has been shown that a standard GNN (such as that in Eq. 1) cannot solve link prediction tasks (Zhang et al 2021). Therefore, using a standard GNN for ``pre-training, adaptation'' when pre-training is link prediction is not ideal, as it will not be able to perform the pre-training task well. I think you either should not consider link prediction as pre-training task (either remove it from the paper or move it to the appendix clarifying why theoretically is not a good strategy given the model) or change the GNN model, as shown in Bevilacqua et al., 2025. 
2. The evaluation section is weak. The considered datasets are small and outdated. I would recommend to include the ogb datasets (arxiv, proteins, products).


Zhang et al., 2021. Labeling trick: A theory of using graph neural networks for multi-node representation learning.

Bevilacqua et al., 2025. Holographic Node Representations: Pre-training Task-Agnostic Node Embeddings

QUESTIONS:
Please connect your subgraph-constrained topology-oriented prompting with the line of work of Subgraph GNNs, which also represents related work. See for instance Southern et al., 2025 and all works therein.


Southern et al., Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 3
DATE: 2025-07-02