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REVIEW 2 major objections 8 minor 1 cited by

Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learning

T0 review · 2 major / 8 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This survey claims that every LLM-for-graph method is either Graph2text or Graph2token, defined by what the model receives.

desk verdict A useful survey framing undercut by a definitional blur: the Graph2text/Graph2token split is a good organizing idea but needs a clearer criterion before the taxonomy can be relied on. read the letter →

arxiv 2501.01124 v1 pith:MPBKYWEN submitted 2025-01-02 cs.LG

Shuo Yu , Yingbo Wang , Ruolin Li , Guchun Liu , Yanming Shen , Shaoxiong Ji , Bowen Li , Fengling Han
show 2 more authors
Xiuzhen Zhang Feng Xia
This is my paper · ORCID
classification cs.LG
keywords graphlearninglargelanguagemodelsGraph2textGraph2tokengraph-to-texttransformationsurveytaxonomytokenization
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This survey of LLM-based graph learning proposes that the entire method landscape is determined by one design choice: what the language model actually receives. The paper's central claim is that every existing method is either Graph2text, which serializes a graph into human-readable strings, or Graph2token, which converts nodes, edges, or subgraphs into token or embedding inputs. It argues that both paradigms confront the same four transformation problems—alignment, position, multi-level semantics, and context—and organizing the literature this way explains why and how LLMs can reason over irregular graph structures. The paper also draws practical conclusions: which paradigm fits which graph type, when prompting versus fine-tuning is cheaper or safer, and which open problems remain.

What carries the argument

The central object is the transformation step from graph to LLM input, and the paper's machinery is a two-armed taxonomy: Graph2text versus Graph2token, with the latter subdivided by token granularity (node, pairwise, group-aware, holistic). Four named challenges—alignment, position, multi-level semantics, and context—serve as the analytic grid that assigns each subclass its problem. The taxonomy carries the argument by converting an unstructured method zoo into a two-by-four design space that the paper then uses to give model-selection and prompting-versus-fine-tuning guidance.

What would settle it

The taxonomy would be settled by an annotation study: take a representative sample of published LLM4graph papers and ask independent annotators to classify each as Graph2text, Graph2token, or both/neither; if a substantial share lands in both/neither or annotators disagree, the dichotomy fails, and the paper's own placements of GraphTranslator and Gpt4graph are immediate candidates to test.

Watch

Extended reading notes

Core claim

The paper's central claim is taxonomic: when a graph is fed to an LLM, the format of that input is the fundamental dividing line in the field the authors call LLM4graph. Graph2text covers methods that translate graph structure into natural language or graph-description languages such as GraphML and query languages like Cypher, preserving interpretability and using the LLM's text-processing strength. Graph2token covers methods that encode graph components as tokens—single nodes, node pairs, node groups, or the whole graph—often via a graph encoder or projection layer, so that structural and semantic information is fused before the LLM sees it. The paper pairs this bipartition with four challenges of graph-to-text transformation (alignment, position, multi-level semantics, context) and uses them as a problem-oriented grid to subclassify and compare methods. If the taxonomy is right, the scattered toolbox of LLM-graph techniques reduces to a small set of design decisions, and the four challenges become a checklist for evaluating any new method.

Load-bearing premise

The dichotomy between Graph2text and Graph2token is exhaustive and mutually exclusive: every LLM4graph method can be assigned unambiguously to exactly one paradigm based on the format of the LLM input.

Editorial extensions

If this is right

  • Any future LLM4graph method can be classified by a single question: does the LLM receive readable text or token/embedding input?
  • The four challenges give a concrete checklist—alignment, position, multi-level semantics, context—for designing graph serializations and for spotting why a given method underperforms.
  • The practical guideline says Graph2text suits textual graphs, AMR graphs, and knowledge graphs where interpretability matters, while Graph2token suits attributed, heterogeneous, community, and molecular graphs where structure must be fused into representations.
  • Prompt-based tricks are preferred when data is scarce or when privacy rules out sending sensitive graph data to external APIs; fine-tuning is preferred when the task demands granular control and customization.
  • The paper's five open problems (general instructions, theoretical invariance, fairness, efficiency and scalability, dynamic graphs) give a roadmap of where the taxonomy stops being prescriptive.

Reading between the lines

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

  • Beyond the paper, the Graph2text/Graph2token distinction is best read as a spectrum: many deployed systems already mix natural-language prompts with projected graph embeddings, so the exhaustive dichotomy is a useful approximation rather than a sharp law.
  • Beyond the paper, the four challenges could be turned into a falsifiable evaluation protocol: report which challenges each method addresses and measure the performance gain when each is explicitly handled.
  • Beyond the paper, the suggestion to serialize graphs with database query languages (Cypher, Gremlin) is directly testable by comparing LLM accuracy on identical graph reasoning tasks across serialization formats.
  • Beyond the paper, the treatment of non-edges as negative semantics suggests a concrete experiment: check whether Graph2token models that explicitly encode disconnected node pairs beat those that encode only observed edges on link prediction benchmarks.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 8 minor

Summary. The manuscript is a survey of methods that apply large language models (LLMs) to graph learning, termed LLM4graph. It argues that all such methods can be partitioned into two paradigms: Graph2text, which converts graphs into human-readable text, and Graph2token, which converts graphs into tokens or embeddings for LLM input. The paper identifies four transformation challenges (alignment, position, multi-level semantics, and context), uses them to organize the methods, provides practical guidance for model selection based on graph type, hardware, and optimization tricks, and outlines five future research directions.

Significance. The paper offers a broad and current collection of LLM4graph methods, a transformation-based perspective that goes beyond task- or architecture-centric surveys, and a practical resource table with hardware and code availability. If the taxonomy were precisely defined, this could become a useful reference for researchers entering the field. The main contribution, however, is the proposed Graph2text/Graph2token dichotomy, and that dichotomy currently lacks a clear operational boundary and contains internally inconsistent method assignments. The paper also compiles useful open problems, but the foundational classification claim needs substantial revision before the survey can serve as a reliable organizing framework.

major comments (2)
  1. [Section 2.2; Section 5.1.1; Section 6.2.1; Table 1; Section 5.3.2] The central dichotomy between Graph2text and Graph2token is not operationally defined. Section 2.2 defines 'tokens' in two incompatible ways: first as tokenizer subword units, then as numerical embeddings derived from auxiliary neural network processing. Since every Graph2text input is also tokenized by the LLM, the phrase 'becomes tokens' cannot separate the paradigms. The method assignments are also inconsistent: GraphTranslator is described in Section 5.1.1 as converting structured graph information into 'natural language instructions comprehensible to LLMs', yet it is categorized under Node2token; Gpt4graph is presented as a GDL/Graph2text method in Section 4.1 but appears as an example of soft prompts in Section 6.2.1 and Table 1; GraphPrompter in Section 5.3.2 feeds LLMs soft prompts that combine node embeddings with text embeddings, spanning both paradigms. The paper needs an explicit decision rule (for example, based on whether the LLM input is human-readable language tokens or continuous vectors inserted into the embedding space) and a reclassification of all methods according to that rule.
  2. [Sections 4 and 5] The claimed use of the four challenges as subclassification criteria is inconsistent with the actual organization. Section 4 subclassifies Graph2text by graph type (general, AMR, KG) and then by challenges, whereas Section 5 subclassifies Graph2token by token granularity (node, pairwise, group, holistic) and then by challenges. Moreover, the challenge dimensions are unevenly covered: Section 5.2 (Pairwise nodes2token) has no Alignment subsection, and Section 5.3 (Group-aware nodes2token) has no Position subsection, while other categories contain different subsets of challenges. A consistent two-level taxonomy (for example, paradigm first, challenge second, then specific methods) is needed for the framework to be usable and for fair comparison across the two paradigms.
minor comments (8)
  1. [Section 5.1.1] The sentence 'This process addresses the problem of modality mismatch between graph data and text data [? ]' contains a placeholder citation '[? ]' that must be replaced with a proper reference.
  2. [Section 4.3] The sentence 'This process aims to align Graph2text properly to ensure that the structured knowledge is being inaccurately converted' is self-contradictory; 'inaccurately' should likely be 'accurately'.
  3. [Section 3] In the discussion of the context problem, 't is worth noting that texts are always regular sequences' is missing the initial 'I'.
  4. [Section 6.2.2] The phrase 'ensuring effective cross-modal intergration' contains a typo: 'intergration' should be 'integration'.
  5. [Section 7.5] The phrase 'perform consistenly' should be 'perform consistently'.
  6. [Table 1] The GraphPrompter row cites reference [127], but the correct reference for GraphPrompter is [125]; reference [127] is MoMu. Please correct the citation.
  7. [Section 1 footnote] The footnote explaining that 'text' refers both to a general modality and to the specific Graph2text representation is confusing; consider defining 'text' and 'token' in a single, unambiguous place, and avoid using 'textual representation' to cover both paradigms.
  8. [Section 6.1.3] The recommendation to use Pairwise nodes2token for heterogeneous graphs is not well supported by the methods cited there (N-GCN, T-PAIR, PLNLP, LPFormer, NAGphormer), which are mostly general link-prediction and node-classification methods; please justify or adjust the recommendation.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the Graph2text/Graph2token taxonomy is a stipulated organizational scheme; the paper's self-citations are background only and no prediction reduces to an input.

full rationale

This is a survey paper with no fitted parameters, no quantitative predictions, and no derivation chain that could reduce to its own inputs. The central contribution is a taxonomic claim: methods are grouped by whether graph information is supplied to an LLM as readable text or as graph-derived token/embedding representations. That distinction is stipulated in Section 1 and applied to external methods; it is not derived from the methods being classified. The four transformation challenges (alignment, position, multi-level semantics, context) are presented as organizational lenses, not as theorems or fitted results. Citations to the authors' own prior work ([2,3,18,107,141]) appear only as background for graph-learning surveys, graph transformers, heterogeneous-graph context, and fairness; none is invoked to justify the taxonomy or to rule out alternatives. There is no imported uniqueness theorem, no ansatz smuggled in via citation, and no fitted input renamed as a prediction. The paper's internal inconsistencies in assigning particular methods (e.g., Gpt4graph appearing in both Graph2text and soft-prompt/Graph2token contexts; GraphTranslator described as producing natural-language instructions yet placed under Node2token in Section 5.1.1) are classification-rule ambiguities rather than circular reasoning, because the taxonomy does not depend on those placements for its definition. A missing citation marker in Section 5.1.1 is a bibliographic omission, not a circular step. Therefore the honest finding is no significant circularity.

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

The review makes no quantitative predictions, so there are no free parameters or invented physical entities. The taxonomy rests on three implicit assumptions: the two-paradigm dichotomy is exhaustive, the four challenges are the right sub-classification axes, and the cited method descriptions are accurate. The first two are ad hoc to this paper, and the third is a domain assumption given the lack of a verification protocol.

assumptions (3)
  • ad hoc to paper All LLM4graph methods can be partitioned into exactly two paradigms, Graph2text and Graph2token, based on whether the transformed input is human-readable text or token/embedding sequences.
    The entire taxonomy depends on this dichotomy; the paper's own assignments are ambiguous, e.g., GraphTranslator converts graphs into natural language instructions yet is listed under Node2token in Section 5.1.1.
  • ad hoc to paper The four transformation challenges (alignment, position, multi-level semantics, context) are the appropriate criteria for subclassification.
    The subclassification sections are uneven: Pairwise nodes2token only discusses position and hierarchy, while Holistic nodes2token only discusses alignment and position; no justification is given for why each method addresses a particular challenge.
  • domain assumption The descriptions of cited methods are accurate and complete enough to support the taxonomy.
    The review does not include a systematic verification protocol; mis-categorizations such as placing Gpt4graph under both hard and soft prompts (Section 6.2.1 vs Table 1) suggest some descriptions may be unreliable.

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Cite this review

Pith. "Pith review of Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learning." pith.science (2026). https://pith.science/paper/MPBKYWEN

@misc{pith2026250101124,
  author       = {Pith},
  title        = {Pith review of: Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MPBKYWEN}},
  note         = {Machine review of arXiv:2501.01124}
}
read the original abstract

Graphs are data structures used to represent irregular networks and are prevalent in numerous real-world applications. Previous methods directly model graph structures and achieve significant success. However, these methods encounter bottlenecks due to the inherent irregularity of graphs. An innovative solution is converting graphs into textual representations, thereby harnessing the powerful capabilities of Large Language Models (LLMs) to process and comprehend graphs. In this paper, we present a comprehensive review of methodologies for applying LLMs to graphs, termed LLM4graph. The core of LLM4graph lies in transforming graphs into texts for LLMs to understand and analyze. Thus, we propose a novel taxonomy of LLM4graph methods in the view of the transformation. Specifically, existing methods can be divided into two paradigms: Graph2text and Graph2token, which transform graphs into texts or tokens as the input of LLMs, respectively. We point out four challenges during the transformation to systematically present existing methods in a problem-oriented perspective. For practical concerns, we provide a guideline for researchers on selecting appropriate models and LLMs for different graphs and hardware constraints. We also identify five future research directions for LLM4graph.

Figures

Figures reproduced from arXiv: 2501.01124 by the authors.

Figure 1
Figure 1. The illustration of LLM4graph. LLMs understand and process various types of graph data through [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. A taxonomy of research on large language models for graph learning. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Challenges in transforming graphs to texts. (a) The alignment problem. (b) The position problem. (c) [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Direct textualisation with Natural language generation and GDL generation. [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Technical approach of the indirect textualization method. [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: Schematic diagram of Graph2token. Converting graph data to tokens in the graph task to help the [PITH_FULL_IMAGE:figures/full_fig_p018_6.png]
Figure 7
Figure 7. Figure 7: Classification of four tokenization methods based on token granularity. [PITH_FULL_IMAGE:figures/full_fig_p019_7.png]

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Forward citations

Cited by 1 Pith paper

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.