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Can Graph Quantization Tokenizer Capture Transferrable Patterns? |
Soundness: 3: good
Presentation: 3: good
Contribution: 2: fair
Rating: 4: marginally below the acceptance threshold
Confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked. |
This paper investigates whether graph quantization tokenizers—specifically those based on vector quantization (VQ) and residual vector quantization (RVQ)—are able to capture transferable structural patterns across heterogeneous graph datasets. The authors introduce a metric called Token Information Discrepancy Score (TIDS) to quantify how consistently the same discrete token corresponds to similar structural and feature patterns in different graphs. They find that existing graph quantizers often map structurally dissimilar node contexts to the same token, leading to reduced transferability in downstream tasks. To mitigate this issue, the paper proposes a Structural Hard Encoding (SHE) strategy intended to inject structural information into the tokenizer. Experiments indicate that SHE reduces TIDS and improves cross-dataset performance to some extent.
1. With recent movement toward graph foundation models and discrete graph tokenization, examining transferability is important and underexplored.
2. The paper clearly states the gap between current quantization practices and cross-domain robustness.
3. The proposed TIDS score provides a simple and interpretable measure for assessing token consistency across datasets.
1. The main contributions are empirical and diagnostic; the proposed SHE method seems incremental and not conceptually strong enough to be considered a substantial methodological advance.
2. The evaluation appears limited in scale—datasets used for analysis and transfer are not clearly representative of the breadth of graph domains where tokenization matters (e.g., molecular vs. social vs. knowledge graphs).
3. The paper focuses on a few quantization models but does not compare against more recent or structurally richer tokenization schemes (e.g., subgraph vocabulary learning, motif-based tokenizers) [1, 2].
[1] Beyond Message Passing: Neural Graph Pattern Machine, ICML 25.
[2] Scalable Graph Generative Modeling via Substructure Sequences, NeurIPS 25.
1. Can the authors elaborate on whether TIDS correlates with transfer performance within the same domain (e.g., differing graphs of similar type)? Or is the effect only present in cross-domain settings?
2. How sensitive is SHE to hyperparameters and model architecture? Could the improvements stem from implicit regularization rather than structural encoding? |
Fully AI-generated |
|
Can Graph Quantization Tokenizer Capture Transferrable Patterns? |
Soundness: 3: good
Presentation: 3: good
Contribution: 3: good
Rating: 6: marginally above the acceptance threshold
Confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked. |
The authors investigates whether the graph quantized tokenizers can capture transferable graph patterns across datasets. To evaluate this, they propose a new metric, i.e., graph token information discrepancy, to measure the consistency of token-level feature and structure information between source and target graphs.
While theoretical analysis proves that lower discrepancy indicates tighter transfer generalization bounds, their empirical studies conducted on two domains reveal that, structural information is poorly aligned across datasets (high structural GTID), while feature information transfers better.
Therefore, they introduce structural hard encoding technique to effiectively reduces GTID.
- the task is novel: while some works use quantization techniques to tokenize graphs, the authors are the first to investigate the transferability problem.
- theoretical analysis and emprical studies further demonstrate the transferable pattern problem.
- a simple yet efficient solution is proposed to boost the transferability.
- the downstream tasks are limited to the classification, more can be explored.
- case studies are encouraged to further demonstrate the effectiveness of the proposed technique
please refer to the weaknesses |
Fully human-written |
|
Can Graph Quantization Tokenizer Capture Transferrable Patterns? |
Soundness: 3: good
Presentation: 2: fair
Contribution: 3: good
Rating: 6: marginally above the acceptance threshold
Confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked. |
This paper focuses on the graph tokenization. Specifically, the authors argue that existing methods cannot capture complex graph structural information. While, the authors further develop a new metric called TIDS to measure the effectiveness of existing token generators. Empirical results based on TIDS reveal the inconsistent issue in previous methods. Finally, the authors provide a solution named SHE for addressing the above issue.
1.This paper is clearly motivated and easy to follow.
2.The authors provide the theoretical analysis of the proposed metric.
3.The proposed TIDS provides new insights for graph quantization tokenizer.
1.The introduction of background is somewhat too long.
2.The empirical results are not extensive.
3.There are several grammar issues.
1.The authors report the results of TIDS on various experimental settings. I just wonder how the performance of each model on the downstream tasks?
2.Does the observed issue have serious impact on the model performance for the specific downstream tasks (node classification or link prediction)?
3.Minor issues, like “TOKENZIER” in the title. Please proofread the manuscript. |
Fully human-written |
|
Can Graph Quantization Tokenizer Capture Transferrable Patterns? |
Soundness: 2: fair
Presentation: 2: fair
Contribution: 2: fair
Rating: 4: marginally below the acceptance threshold
Confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. |
This paper investigates whether graph quantized tokenizers (using VQ/RVQ methods) capture transferable structural patterns across different graph domains. The authors introduce the Graph Token Information Discrepancy Score (GTID), a metric based on normalized Maximum Mean Discrepancy that quantifies the alignment of structural and feature information for nodes assigned to the same token across source and target graphs.
Novelty : Investigating whether graph tokenizers learn reusable structural patterns addresses a fundamental gap as the community moves toward graph foundation models. The motivation is well-articulated and grounded in practical applications.
Motivation and Analysis: GTID provides an intuitive decomposition of token consistency into feature-based and structure-based components. The separation enables targeted analysis of tokenizer failures and could inform future design choices. Theorems 1 and 2 formalize the intuition that code-conditional discrepancies in feature/structural spaces directly contribute to transfer error. The bounds connect IPM-based discrepancies (W₁ distance for features, MMD for structures) to generalization gaps .
Empirical Results: SHE show measurable reductions in structural GTID and performance gaps (Figure 5) which validate the hypothesis that explicit structural inductive biases matter.
Dataset:
- Only citation networks and e-commerce graphs are tested. More critical domains like molecular graphs (where structural motifs determine properties), biological networks, and social networks are absent. Largest graph has 173K nodes, which is small. GTID's behavior and computational feasibility at scale (millions of nodes/edges) is unknown. From my understanding, GTID requires computing centrality for every node, which may prohibitively expensive for large graphs.
Baselines: There are a lot of missing baselines from this paper.
- No non-quantized transfer baselines: The paper does not compare against established continuous GNN transfer learning approaches such as GraphCL [1], SimGRACE [2], or GROVER [3] for molecular graphs, nor domain adaptation methods like AdaGCN [4] or UDAGCN [5]. Without these baselines, it is impossible to determine whether the observed GTID-related issues are fundamental limitations of discretization or simply artifacts of suboptimal VQ/RVQ implementation. The paper does not justify why quantization is necessary when continuous fine-tuning remains a viable alternative.
- Alternative tokenizer comparisons: The paper focuses exclusively on VQ/RVQ but does not compare to other graph tokenization paradigms mentioned in related work, such as GFT's transferable tree vocabulary [6], OneForAll's task-level tokenization [7], or subgraph-mining approaches [8]. This makes it unclear whether high structural GTID is specific to vector quantization methods or a general challenge across all discrete graph representations.
- Single encoder architecture: Only MPNN encoders are tested. Recent work has shown that Graph Transformers with structural encodings [9, 10, 12] and attention-based architectures like GPS [11] exhibit fundamentally different inductive biases. These architectures may interact with quantization differently. For instance, global attention mechanisms might better preserve structural information during discretization, or conversely, might suffer more from quantization artifacts.
[1] Zhu et al., Graph Contrastive Learning with Augmentations, NeurIPS 2020.
[2] Xia et al., SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation, WWW 2022.
[3] Rong et al., Self-Supervised Graph Transformer on Large-Scale Molecular Data, NeurIPS 2020.
[4] Dai et al., Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolution, IEEE TKDE 2022.
[5] Wu et al., Unsupervised Domain Adaptive Graph Convolutional Networks, WWW 2020.
[6] Wang et al., GFT: Graph Foundation Model with Transferable Tree Vocabulary, NeurIPS 2024.
[7] Liu et al., One for All: Towards Training One Graph Model for All Classification Tasks, ICLR 2024.
[8] Jin et al., Towards Graph Foundation Models: A Survey and Beyond, arXiv 2024.
[9] Rampaśek et al., Recipe for a General, Powerful, Scalable Graph Transformer, NeurIPS 2022.
[10] Rampášek et al., Exphormer: Sparse Transformers for Graphs, ICML 2023.
[11] Rampaśek et al., GraphGPS: General Powerful Scalable Graph Transformers, NeurIPS 2022.
[12] Ling et al., UNIFIEDGT: Towards a Universal Framework of Transformers in Large-Scale Graph Learning, IEEE BigData 2024.
Presentation:
- TIDS (abstract) vs GTID (body)? Are they referring to the same thing? They are quite confusing to me.
- K for both codebook size and function K(g)
- Eq. 3's L×M tuple vs. VQ/RVQ distinction not explained
- "co_pu" to "ar_db" in Figures 2,3,6,7 what do they mean?
- Only figures for quantitative comparisons
- Typos: "tokenizer" (title), "langugae", "the are no"
- RKHS kernel in Theorem 2 not stated
Reproducibility:
- No code provided
See weakness |
Lightly AI-edited |