Title: One for All: Universal Topological Primitive Transfer for Graph Structure Learning
Venue: NeurIPS 2025
Accepted: yes
Keywords: Graph Structure Learning

Transfer Learning.
Abstract: The non-Euclidean geometry inherent in graph structures fundamentally impedes cross-graph knowledge transfer. Drawing inspiration from texture transfer in computer vision, we pioneer topological primitives as transferable semantic units for graph structural knowledge. To address three critical barriers - the absence of specialized benchmarks, aligned semantic representations, and systematic transfer methodologies - we present G²SN-Transfer, a unified framework comprising: (i) TopoGraph-Mapping that transforms non-Euclidean graphs into transferable sequences via topological primitive distribution dictionaries; (ii) G²SN, a dual-stream architecture learning text-topology aligned representations through contrastive alignment; and (iii) AdaCross-Transfer, a data-adaptive knowledge transfer mechanism leveraging cross-attention for both full-parameter and parameter-frozen scenarios. Particularly, G²SN is a dual-stream sequence network driven by ordinary differential equations, and our theoretical analysis establishes the convergence guarantee of G²SN. We construct STA-18, the first large-scale benchmark with aligned topological primitive-text pairs across 18 diverse graph datasets. Comprehensive evaluations demonstrate that G²SN achieves state-of-the-art performance on four structural learning tasks (average 3.2\% F1-score improvement), while our transfer method yields consistent enhancements across 13 downstream tasks (5.2\% average gains) including 10 large-scale graph datasets. The datasets and code are available at https://anonymous.4open.science/r/UGSKT-C10E/.


Peer reviews:
comments: SUMMARY:
This paper presents G²SN-Transfer, a framework for universal graph structure learning. It transforms non-Euclidean graphs into transferable sequences using topological primitive distribution dictionaries, learns aligned representations via a dual-stream G²SN architecture, and enables adaptive knowledge transfer through AdaCross-Transfer. The framework is validated on the STA-18 benchmark, showing state-of-the-art performance on structural learning tasks and consistent improvements across downstream applications.

STRENGTHS AND WEAKNESSES:
Strengths:
- Proposes topological primitives as transferable units, bridging non-Euclidean graph geometry for cross-domain knowledge transfer.  
- Develops G²SN, a dual-stream network with ODE-driven dynamics, theoretically guaranteed to converge and capture structural-textual dependencies.  
- Creates STA-18, the first large-scale benchmark with aligned topological-text pairs, enabling rigorous evaluation of graph transfer methods.  

Weaknesses:
- Topological primitives are limited to two-hop neighborhoods, potentially missing higher-order structural patterns.  
- LLM-generated annotations require manual validation, introducing human oversight in dictionary construction.

QUESTIONS:
- How does the framework perform on graphs with dynamic topological changes over time?  
- Can the transfer mechanism be extended to heterogeneous graphs with diverse node/edge types?

LIMITATIONS:
The computational overhead of constructing topological-text annotations increases with graph size, affecting scalability to extremely large datasets. The reliance on LLM annotations may introduce bias if textual descriptions do not fully capture structural nuances.
TITLE: Official Review
CLARITY: 4
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-30

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a method for universal topological primitive transfer in graph structure learning, introducing an approach based on State Space Models (SSM) for addressing domain generalization in graph-based tasks. The model aims to pre-train on large-scale graphs and transfer learned structures to smaller graphs with domain shifts. The paper includes theoretical analysis based on SSM and evaluates the model on several standard datasets.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. Interesting Problem Domain: The idea of domain generalization in graph learning is an interesting area of exploration. The attempt to leverage pre-trained topological features across different domains is valuable for advancing the field.\n\n2. Model Novelty: The proposed method introduces a unique combination of SSM-based modeling with graph structure transfer, which has the potential to lead to advancements in graph learning.\n\nWeaknesses:\n\n1. Lack of Strong Motivation for Using SSM\n\nThe paper introduces State Space Models (SSM) without sufficiently explaining why SSM is necessary for graph structure learning. Traditional GNN models or Graph Transformers could potentially handle the graph structure transfer task without the complexity introduced by SSM. The lack of motivation for choosing SSM over more standard models makes the innovation feel less compelling.\n\n2. No Domain Generalization Theory or Analysis\n\nDespite the paper's claim of addressing domain generalization, it lacks any formal theory or analysis regarding generalization bounds or invariant representation in the context of graph learning. There is no mention of foundational work such as IRM (Invariant Risk Minimization), domain adaptation theory, or generalization bounds. This gap makes it difficult to assess the actual effectiveness of the proposed approach in handling domain shifts.\n\n3. Missing Comparison to Existing Domain Generalization Works\n\nThe paper does not cite or compare the proposed method to any established domain generalization approaches in the context of graph learning, such as the Multi-Domain Graph Foundation Model. Without this context, it is unclear how this method advances or improves upon the current state-of-the-art in domain generalization for graph data [1][2].\n\n[1] Yu X, Gong Z, Zhou C, et al. Samgpt: Text-free graph foundation model for multi-domain pre-training and cross-domain adaptation. Proceedings of the ACM on Web Conference 2025.\n\n[2] Wang S, Wang B, Shen Z, et al. Multi-domain graph foundation models: Robust knowledge transfer via topology alignment. ICML 2025.\n\nQUESTIONS:\nSee Weaknesses.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces G²SN-Transfer, a “one-for-all” framework that treats recurring topological primitives (small, statistically frequent sub-structures) as transferable units of graph knowledge. It first serializes any graph into a dual sequence of (i) motif-distribution codes and (ii) LLM-generated textual descriptors via TopoGraph-Mapping, creating the 18-dataset STA-18 benchmark. A dual-stream state-space model (G²SN) is then pre-trained to align topology and text with a provably convergent formulation. Finally, AdaCross-Transfer injects these representations into downstream GNNs through parameter-efficient cross-attention, yielding significant gains on long-range structure tasks and transfer benchmarks.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n- The concept of topological primitives as graph textures is interesting and novel, bridging the gap between non-Euclidean data and textual information.\n- STA-18 is the first (to my knowledge) graph-text benchmark, which should benefit the future researcher.\n- The proposed State space model is backed by closed-form solutions and guaranteed convergence.\n- Results show promising and consistent improvement over previous baselines. \n\n## Weaknesses\n- Automatically generated textual descriptors may embed bias or noise, yet no human evaluation or further verification confirms their fidelity or usefulness for transfer.\n- It is unclear how the theoretical time/space complexity and the empirical runtime of G²SN-Transfer compare with standard GNN baselines such as GCN and GraphSAGE, especially when all components (motif extraction, dual-stream encoder, AdaCross) are included.\n- It is unclear how the number of primitives is selected, what exact algorithm is used to compute each graph’s motif-frequency vector, and how the computation scales (e.g., to k-node motifs) on large graphs.\n\nQUESTIONS:\n- In Figure 3, which is the textual sequence $u$ and LLM-Topomotif sequence $m$?\n- How might the learned topological-textual latent space enable inverse problems—for example, generating synthetic graphs that satisfy a natural-language specification of desired structural properties?\n- What theoretical or empirical considerations led to a state-space model over other more common graph-aware transformers or message-passing models? Have you evaluated G²SN against a parameter-matched Transformer encoder that is fed the same motif distribution and text embeddings? \n- Motif distributions are treated as fixed-length vectors of size $M$. Did you test the sensitivity of the whole pipeline to $M$ (e.g., 16 vs. 64 primitives)?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

comments: SUMMARY:
The manuscript introduces **G²SN-Transfer**, a three-stage framework that serialises graph substructures into textual “topological primitives,” injects the learned knowledge into downstream GNNs through a lightweight cross-attention adapter. 

On the new STA-18 benchmark, the method achieves an average 3.2% F1 improvement over strong long-range baselines.

STRENGTHS AND WEAKNESSES:
### Strengths

* **Technical soundness**: Provides closed-form solutions and convergence guarantees for the state-space backbone.

* **Empirical rigor**: Demonstrates consistent performance boosts on 18 datasets and releases code and data for reproduction.

### Weaknesses

* The fixed dictionary of seven primitives may underspecify higher-order motifs, limiting generality.

* Theoretical analysis assumes linear time-invariant dynamics; applicability to dynamic or weighted graphs remains unverified.

* The writing requires non-trivial improvement. There are too many rare words/complex sentences used in the paper, which does not align with the goal of academic writing. 

* Representative GFM + LLM studies (related works) not discussed sufficiently.

QUESTIONS:
1. How sensitive are the results to the chosen set of seven primitives? Would including non-cyclic motifs such as stars or cliques improve transfer?

2. LLM-generated descriptors can vary stylistically; what mechanisms ensure annotation consistency?

3. Have you observed negative transfer on heterogeneous datasets ?

LIMITATIONS:
Please refer to the questions.
TITLE: Official Review
CLARITY: 2
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-03

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper \"One for All: Universal Topological Primitive Transfer for Graph Structure Learning\" introduces a novel framework, GSN-Transfer, for transferring structural knowledge across diverse graph datasets by leveraging topological primitives as transferable semantic units. It addresses challenges in cross-graph knowledge transfer due to the non-Euclidean nature of graphs through three main contributions:\n\n1. **TopoGraph Mapping**: This component converts non-Euclidean graph structures into sequential representations using topological primitive distribution dictionaries, enabling the creation of the STA-18 benchmark dataset, a diverse graph-text dataset spanning 18 graphs.\n\n2. **GSN (Graph Structure Network)**: A dual-stream neural network architecture that learns aligned text-topology representations through contrastive alignment and large-scale pre-training on the UniKG-STA dataset. It captures transferable structural invariants, supporting both full-parameter fine-tuning and frozen parameter strategies.\n\n3. **AutoCross-Transfer**: A data-adaptive knowledge transfer mechanism that uses cross-attention to merge universal and dataset-specific graph structure semantics, facilitating effective transfer across domains under full-parameter and frozen-and-fine-tuned scenarios.\n\nThe framework achieves state-of-the-art performance, demonstrating a 3.2% average F1 score gain across 16 structural tasks and a 5.2% average gain in 13 downstream applications. Extensive experiments on 36 datasets validate GSN-Transfer’s efficacy in learning transferable structural representations, enhancing downstream task performance, and handling computational efficiency. The paper also provides comprehensive documentation, including dataset details, hyperparameters, and reproducibility instructions, alongside a new dataset release.\n\nSTRENGTHS AND WEAKNESSES:\nQuality\n**Strengths**:\n- **Robust Experimental Design**: The manuscript demonstrates comprehensive empirical validation, evaluating the GSN-Transfer framework across 36 datasets (18 graph datasets and 18 corresponding STA datasets) spanning domains such as citation networks (e.g., Cora, Pubmed) and biological networks (e.g., ogbn-protein) (Page 12). Quantitative results indicate a 3.2% mean F1 score improvement across 16 structural tasks and a 5.2% mean improvement across 13 downstream applications (Page 3), supported by performance metrics (AP, MAEL, F1 Score, Accuracy) in Table 1 (Page 8). These results are benchmarked against established baselines (GCN, GIN), affirming the framework’s efficacy.\n- **Theoretical Rigor**: The inclusion of Theorem 5.1 (Page 6), which establishes Lipschitz-continuous gradient flow for GSN’s compositional output, provides a formal foundation for cross-dataset knowledge transfer. The proof, detailed in Appendix C (Page 14), leverages state space models (SSMs) and is mathematically sound, enhancing the work’s credibility. Spectral normalization and K-Lipschitz similarity functions (Page 16) further ensure stable gradient dynamics.\n- **Reproducibility**: The manuscript adheres to reproducibility standards, providing an anonymized link to datasets and code (Page 21), hyperparameter details in Appendix H and Table 7 (Page 18), and computational resource specifications (e.g., single 24GB GeForce RTX 4090 GPU, Page 19). These elements align with NeurIPS guidelines, facilitating independent verification.\n\n**Weaknesses**:\n- **Limited Baseline Comparisons**: The evaluation includes only GCN and GIN as baselines (Table 1, Page 8), omitting comparisons with contemporary graph neural network (GNN) architectures (e.g., GraphSAGE, GAT) or transfer learning methods (e.g., GraphCL, DANN). This restricts the ability to position GSN-Transfer’s performance relative to the state-of-the-art, potentially undermining claims of superiority (Page 3).\n- **Insufficient Ablation Analysis**: Ablation studies (Table 3, Page 9) are conducted on only six datasets and focus solely on transfer methods, neglecting to dissect contributions of key components (e.g., TopoGraph Mapping, dual-stream architecture, motif selection). This limits the evidence for the necessity and optimality of each module.\n- **Incomplete Complexity Analysis**: While computational complexity is briefly addressed (Page 18), the manuscript lacks quantitative metrics (e.g., time complexity for pre-training or inference). Given the framework’s reliance on large-scale pre-training and cross-attention, this omission raises concerns about scalability and practical deployment.\n\n**Accept/Reject Rationale**: The extensive empirical validation and theoretical grounding strongly support acceptance, reflecting high scientific quality. However, the limited baseline comparisons and shallow ablation studies warrant minor revisions to substantiate claims of state-of-the-art performance and component efficacy.\n\n Clarity\n**Strengths**:\n- **Structured Exposition**: The manuscript is organized logically, progressing from preliminaries (Section 2, Page 3) to methodology (Sections 3–5, Pages 3–6) and experiments (Section 6, Page 7). Visual aids, including Figure 1 (GSN-Transfer pipeline, Page 2) and Figure 4 (AdaCross-Transfer, Page 5), effectively illustrate complex mechanisms, enhancing comprehension.\n- **Detailed Technical Descriptions**: Key components are articulated with precision. TopoGraph Mapping is defined as transforming non-Euclidean graphs into sequential representations via topological primitive distribution dictionaries (Page 1). The GSN dual-stream architecture, incorporating contrastive alignment, is similarly well-specified (Page 1). Algorithm 1 (Page 4) and the annotation generation algorithm (Figure 2, Page 3) provide clear procedural details.\n- **Transparent Reporting**: The NeurIPS checklist (Pages 20–26) is thoroughly completed, addressing reproducibility, experimental settings, and compute resources (Pages 21–23). This transparency ensures that methodological and empirical claims are accessible to reviewers.\n\n**Weaknesses**:\n- **Specialized Terminology**: Terms such as “topological primitives,” “structural textures,” and “motif parallel statistics” are introduced without sufficient introductory explanation (Pages 1–2). This may hinder accessibility for readers unfamiliar with graph structure learning, necessitating clearer definitions or examples.\n- **Content Truncation**: The OCR output indicates significant truncation (e.g., 7187 characters on Page 1, 10173 on Page 4), potentially omitting critical methodological or experimental details. While core concepts are conveyed, these gaps could obscure full understanding of proofs (Page 14) or pre-training protocols (Page 17).\n- **Over-Reliance on Appendices**: Critical details, such as GSN block implementation (Page 17) and hyperparameter configurations (Appendix H, Page 23), are deferred to appendices. This disrupts the main text’s self-containment, potentially challenging readers seeking a cohesive narrative.\n\n**Accept/Reject Rationale**: The structured exposition and detailed figures support acceptance by ensuring methodological clarity. However, dense terminology and appendix reliance suggest minor revisions to enhance accessibility and narrative flow. Truncation, if present in the original submission, could justify a conditional acceptance pending clarification.\n\nSignificance\n**Strengths**:\n- **Addressing a Critical Challenge**: The manuscript tackles a pressing issue in graph structure learning: the difficulty of cross-graph knowledge transfer due to non-Euclidean geometries (Page 1). By proposing topological primitives as transferable semantic units, it offers a generalizable solution applicable to diverse domains, from citation to biological networks.\n- **Broad Impact Potential**: The STA-18 dataset (Page 3), spanning 18 graph-text pairs, is a significant contribution, providing a novel benchmark for graph transfer learning. Its diversity and public release (Page 25) enhance its utility for the research community. The 5.2% downstream task improvement (Page 3) underscores practical relevance for applications like node classification and link prediction (Page 12).\n- **Scalable Framework**: The GSN-Transfer framework’s ability to operate under both full-parameter fine-tuning and frozen-and-fine-tuned scenarios (Page 6) ensures flexibility for resource-constrained settings, increasing its applicability in real-world scenarios.\n\n**Weaknesses**:\n- **Narrow Task Focus**: The evaluation primarily focuses on structural tasks (e.g., motif prediction) and standard graph tasks (node classification, link prediction) (Page 7). The manuscript does not explore more complex applications, such as graph generation or temporal graph analysis, limiting the perceived breadth of impact.\n- **Dataset Scale Concerns**: While STA-18 is diverse, the manuscript does not discuss the scale of individual datasets (e.g., node/edge counts beyond Table 4, Page 12) or their representativeness of large-scale real-world graphs. This raises questions about generalizability to massive graphs encountered in industry settings.\n- **Limited Discussion of Societal Impact**: The NeurIPS checklist (Page 25) claims no societal risks, but the manuscript does not explore potential implications (e.g., biases in graph-based recommendation systems). A brief discussion could strengthen its relevance to responsible AI research.\n\n**Accept/Reject Rationale**: The novel dataset and broad applicability strongly support acceptance, addressing a significant gap in graph learning. However, the narrow task focus and lack of societal impact discussion suggest minor revisions to clarify the framework’s scope and ethical considerations.\n Originality\n**Strengths**:\n- **Novel Conceptual Framework**: The use of topological primitives as transferable semantic units, analogous to texture units in visual transfer (Page 2), is a highly original contribution. This abstraction, combined with the distribution of primitives as “structural textures,” introduces a new paradigm for graph knowledge transfer.\n- **Innovative Architecture**: The GSN dual-stream architecture, integrating text-topology alignment via contrastive learning (Page 1), is a distinctive approach. The AutoCross-Transfer mechanism, leveraging cross-attention for adaptive knowledge transfer (Page 5), further differentiates the work from existing GNN-based transfer methods.\n- **New Benchmark**: The creation of STA-18, a graph-text benchmark with 18 diverse datasets (Page 3), is a novel contribution. Its integration of motif-based annotations generated via ChatGPT-4.0 (Page 3) is an innovative application of large language models to graph learning.\n\n**Weaknesses**:\n- **Incremental Relation to Prior Work**: While the topological primitive concept is novel, the manuscript builds on existing ideas like state space models (SSMs) (Page 3) and graph motif analysis (Page 10, references to Choromanski et al., 2020). The distinction from prior motif-based methods (e.g., Graph2Vec, Chen & Koga, 2019) is not fully clarified, raising questions about incremental novelty.\n- **Limited Engagement with Transfer Learning Literature**: The manuscript cites transfer learning broadly (e.g., Pan & Yang, 2019, Page 10) but does not deeply engage with graph-specific transfer learning works (e.g., GraphAdapter, Li et al., 2023). A clearer differentiation from these methods would strengthen the originality claim.\n- **Dependence on Existing Tools**: The use of ChatGPT-4.0 for annotation generation (Page 3) relies on an external tool, which, while innovative, reduces the methodological novelty of the annotation process itself.\n\n**Accept/Reject Rationale**: The novel conceptual framework and STA-18 dataset strongly support acceptance, marking a significant departure from existing methods. However, the incremental relation to prior motif-based work and limited engagement with graph transfer learning literature suggest minor revisions to better articulate the work’s unique contributions.\n\nQUESTIONS:\nBelow are 3–5 actionable questions and suggestions for the authors of \"One for All: Universal Topological Primitive Transfer for Graph Structure Learning,\" focusing on critical areas to address limitations and clarify key points. Each includes clear guidance and criteria for how responses could influence the evaluation score, facilitating a productive rebuttal phase.\n\n1. **Expand Baseline Comparisons**\n   - **Question/Suggestion**: The evaluation in Table 1 (Page 8) compares GSN-Transfer only to GCN and GIN. Could the authors include comparisons with recent GNN architectures (e.g., GraphSAGE, GAT) and graph transfer learning methods (e.g., GraphCL, DANN) across key datasets? Please provide updated performance metrics or justify the omission.\n   - **Guidance**: Conduct experiments on at least 2–3 additional baselines, reporting metrics like F1 Score and Accuracy on datasets like Cora and ogbn-protein. Alternatively, explain why these baselines are less relevant, citing specific architectural or methodological differences.\n   - **Criteria for Score Change**: Including robust comparisons demonstrating GSN-Transfer’s superiority (e.g., >2% F1 improvement) would strengthen the quality and significance, potentially raising the score from “Accept with Minor Revisions” to “Strong Accept.” Omitting this or providing weak justifications could lower the score to “Weak Accept” due to insufficient benchmarking.\n\n2. **Deepen Ablation Studies**\n   - **Question/Suggestion**: The ablation studies (Table 3, Page 9) cover only six datasets and focus on transfer methods. Could the authors expand these to evaluate the contributions of TopoGraph Mapping, dual-stream GSN, and AutoCross-Transfer individually, across a broader dataset range? Please report quantitative impacts (e.g., F1 Score changes).\n   - **Guidance**: Perform ablations by disabling/removing each component (e.g., replacing TopoGraph Mapping with a simpler embedding) on at least 10 datasets, including STA-18. Provide a table summarizing performance drops or gains.\n   - **Criteria for Score Change**: Comprehensive ablations showing significant performance degradation without each component (e.g., >5% F1 drop) would bolster the quality and originality, reinforcing the necessity of the proposed design and potentially elevating the score. Superficial or inconclusive ablations could maintain or lower the score, signaling design weaknesses.\n\n3. **Clarify Terminology and Enhance Accessibility**\n   - **Question/Suggestion**: Terms like “topological primitives” and “structural textures” (Pages 1–2) are introduced without intuitive explanations. Could the authors provide clearer definitions or examples in the introduction or Section 2, targeting readers unfamiliar with graph learning? Please revise the text to include these clarifications.\n   - **Guidance**: Add 1–2 sentences or a brief example (e.g., comparing topological primitives to visual texture patches) in the introduction. Ensure definitions are self-contained in the main text, reducing reliance on external references.\n   - **Criteria for Score Change**: Clear, accessible explanations improving readability for a broader audience would enhance clarity, supporting a higher score (e.g., “Strong Accept”). Persistent jargon without clarification could maintain or lower the score to “Weak Accept” due to reduced accessibility.\n\n4. **Address Computational Complexity**\n   - **Question/Suggestion**: The complexity analysis (Page 18) lacks quantitative details. Could the authors provide time complexity (e.g., O-notation) and empirical runtime metrics for pre-training and inference on a representative dataset (e.g., Cora)? Please include these in Section 6 or an appendix.\n   - **Guidance**: Compute theoretical complexity for key operations (e.g., motif statistics, cross-attention) and report runtimes (e.g., hours/epoch) on a standard GPU. Compare with GCN/GIN to contextualize efficiency.\n   - **Criteria for Score Change**: Detailed complexity metrics showing competitive efficiency (e.g., comparable or faster than GIN) would strengthen quality and practical relevance, potentially raising the score. Lack of specifics or evidence of high complexity could lower the score, raising scalability concerns.\n\n**Overall Impact on Evaluation**: Addressing these points with rigorous experiments, clear revisions, and quantitative evidence would solidify the manuscript’s claims, potentially elevating the evaluation to “Strong Accept” by demonstrating superior performance, robust design, and accessibility. Incomplete or unconvincing responses could lower the score to “Weak Accept” or “Reject” if critical gaps (e.g., weak baselines, unclear methodology) persist. The authors are encouraged to prioritize baseline comparisons and ablations, as these directly impact the perceived quality and originality.\n\nLIMITATIONS:\nIn the \"Limitations\" section, explicitly address 1–2 technical limitations (e.g., scalability or sensitivity to graph heterogeneity) with brief explanations of their implications.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-08"}