Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks

Guoren Wang; Hanwen Cui; Jiayi Wu; Jishuo Jia; Rong-Hua Li; Xunkai Li; Zhengyu Wu

REVIEW 4 major objections 5 minor 2 cited by

Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This survey proposes a Data-Model-Task framework for GNN-LLM integration and argues that text-attributed graphs make text the medium for cross-domain graph generalization.

desk verdict A useful but unreliable map of a hot field: the taxonomy that is supposed to be the paper's contribution is contradicted by the paper's own lists, and the heavy reliance on anonymous under-review papers makes it unusable as a reference in its current form. read the letter →

arxiv 2412.12456 v1 pith:M2W6VPUR submitted 2024-12-17 cs.LG cs.AIcs.CLcs.DB

Xunkai Li , Zhengyu Wu , Jiayi Wu , Hanwen Cui , Jishuo Jia , Rong-Hua Li , Guoren Wang This is my paper · ORCID

classification cs.LGcs.AIcs.CLcs.DB

keywords graphneuralnetworkslargelanguagemodelstext-attributedgraphsfoundationcross-domaingeneralizationreasoningtaxonomymachinelearningsurvey

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 paper surveys the growing body of work that combines graph neural networks (GNNs) with large language models (LLMs), and offers a way to organize it. Its central claim is that the three classic pillars of machine learning—data, model, and task—are the right lenses for understanding GNN-LLM integration. The paper argues that text-attributed graphs make text a common medium: a model that reads node and edge descriptions in natural language can transfer across domains that would otherwise require separate models. If the framework holds, it gives researchers a shared map of the field and a concrete route toward a graph foundation model.

What carries the argument

The central object is the Data-Model-Task classification framework itself. It organizes every included method into one of five data categories (single-task & single-domain, single-task & multi-domain, multi-task & single-domain, multi-task & multi-domain, and graph reasoning), one of five model categories (independent modules, GNN-enhanced LLM, LLM-enhanced GNN, GNN-only, and LLM-only), and one of five training and application categories (single-domain supervised, single-domain unsupervised, multi-domain supervised, multi-domain unsupervised, and few-shot and zero-shot inference). The framework does the argument's load-bearing work: it is what lets the survey claim that text can serve as a universal medium, because the same textual descriptions can be passed through any of the five model designs and evaluated across all three axes.

What would settle it

Take a Multi-task & Multi-domain model from the survey and transfer it to a held-out text-attributed graph whose text is rich but whose structure follows an unusual distribution, such as mostly heterophilic edges or very long-range dependencies. If performance collapses to near-random while a domain-specific GNN trained on that graph keeps high accuracy, the claim that text alone can carry cross-domain generalization would be refuted; if the model transfers, the text-as-medium thesis survives.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that the GNN-LLM literature is not a scattering of ad hoc hybrids but a coherent space that can be classified along three dimensions. From the data side, LLMs supply high-quality semantic features for text-attributed graphs, improving data quality and enabling cross-domain generalization. From the model side, the paper distinguishes five architectures: GNN and LLM as independent collaborative modules, GNN-enhanced LLM, LLM-enhanced GNN, GNN-only, and LLM-only, with learnable integration seen as the path to a graph foundation model. From the task side, it identifies five training-and-application scenarios, from single-domain supervised fine-tuning to multi-domain unsupervised learning and few-shot and zero-shot inference. The unifying thesis is that text is the medium that lets a single graph model handle diverse tasks across different data domains.

Load-bearing premise

The framework assumes that every method fits exactly one of the five data categories, one of the five model categories, and one of the five training categories—an assumption the survey's own lists strain, since methods such as GraphBridge are placed in several cells.

Editorial extensions

If this is right

If text is a workable common medium, then graph models should be designed and evaluated for cross-domain transfer from the start, rather than trained per domain, and multi-task & multi-domain methods become the main line of development.
The five model categories give practitioners a design menu: choose independent modules for simplicity, GNN-enhanced LLM when reasoning is primary, LLM-enhanced GNN when structure is primary, and the learnable options when aiming at a graph foundation model.
The five training categories imply a matching rule: supervised fine-tuning suits single-domain deployments, while generalizable systems need unsupervised multi-domain pre-training followed by few-shot or zero-shot inference.
Graph reasoning is presented as the frontier where integrated models move beyond classification and prediction toward inference and question answering over graph structure.

Reading between the lines

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

Beyond the paper: if text is truly a universal medium, a graph foundation model should be stress-tested on graphs with sparse or noisy text, such as user-generated reviews or low-resource languages, where LLM features degrade; the survey does not single out this failure mode.
Beyond the paper: the taxonomy could be operationalized as a machine-readable registry where each method is tagged with coordinates on the three axes; such a registry would automatically expose overlaps like GraphBridge's dual placement, turning the taxonomy from a static survey into a living map.
Beyond the paper: the model-axis distinctions suggest a concrete experiment—hold the training scenario fixed and compare GNN-enhanced LLM versus LLM-enhanced GNN on the same text-attributed graph benchmark; the survey lists both as promising but does not specify when one should be preferred.
Beyond the paper: the graph-reasoning category implies that evaluation should move beyond node classification to question-answering and link-prediction settings; a next step would be a cross-domain graph-reasoning benchmark that combines the multi-domain datasets in the paper's tables with natural-language graph queries.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Bottom line: this is a handy pointer into a fast-moving field, but it is not yet a reliable systematic survey. The taxonomy that is supposed to be the paper's main contribution is self-contradictory. The same methods land in mutually exclusive categories: GraphBridge appears in both Single-task & Single-domain and Multi-task & Multi-domain in Section 2, and in both independent collaborative modules and GNN-only in Section 4; GraphFM sits in both GNN-only and LLM-enhanced GNN in Section 4; GOFA is cited as [43] in Section 2 and [78] in Section 4. These are not borderline cases. The paper claims a novel, systematic classification framework in Section 8, and that claim is unsupported by its own lists.

Credit where it is due: the paper gathers a broad slice of the recent GNN-LLM literature and provides dataset statistics plus short method summaries. The open-source repo is a genuinely useful index. For a newcomer, skimming this survey gives a quick sense of the landscape. That utility is real, but it is the utility of a blog post, not a foundational reference.

The soft spots are serious. The 15 anonymous under-review references ([1]–[15]) are a load-bearing problem: some of the most-cited methods in the survey (GraphBridge, GraphFM, GraphProp) are exactly these unverifiable entries. When a survey cites work that cannot be checked, the reader cannot trust the categories, the counts, or even whether two entries are the same method. There are also plain citation errors: SKETCH is cited as [40] when [40] is GraphAdapter, and TAGA appears as [87] in Section 2 but [57] in Section 6. These may seem like small slips, but in a survey the citations are the data, and this data is noisy.

The Data-Model-Task trichotomy is standard machine learning vocabulary, and similar LLM-as-enhancer/collaborator/predictor splits already circulate in the field. The paper does not compare its taxonomy with existing surveys, so the novelty claim is not established. If the authors are willing to do that positioning, fix the category assignments, and replace the anonymous references with published versions, a useful survey could emerge from this draft. As it stands, I would not cite it in my own work and I would not hand it to a student as a reliable map.

Verdict: desk reject in current form, but invite a resubmission after major revision. The topic is timely, the material is there, and the flaws are fixable in principle. A serious referee could help the authors see where the taxonomy needs tightening, so in that sense it deserves referee time.

Referee Report

4 major / 5 minor

Summary. This manuscript is a survey of methods that integrate Graph Neural Networks (GNNs) and Large Language Models (LLMs) for learning on text-attributed graphs. The authors propose a classification framework built on three dimensions—Data, Model, and Task—and they assign the surveyed methods to five categories in each dimension (e.g., Section 2 for data, Section 4 for model architectures, Section 6 for training/application scenarios). The paper also tabulates datasets (Section 3), gives short method descriptions (Section 5), and discusses pre-training, fine-tuning, and inference phases (Section 7). The central claim, stated in the abstract and Section 8, is that this is a novel, systematic classification framework that can serve as a foundational reference for the field and that text can act as a medium for cross-domain generalization of graph learning models.

Significance. A reliable survey organizing the rapidly growing GNN-LLM literature would be valuable, and the choice of the Data-Model-Task triad is a reasonable organizing principle. The paper also provides useful dataset tables and maintains an open-source repository, which are helpful community resources. However, the significance of the contribution depends entirely on the validity and reproducibility of the proposed taxonomy. The manuscript's own lists violate the exclusivity of its categories and contain citation inconsistencies, so the claimed 'novel classification framework' is not currently supported. Because the core contribution is the taxonomy, these problems are load-bearing rather than cosmetic.

major comments (4)

[§2 vs §4] The proposed categories are not mutually exclusive as applied. GraphBridge is listed under both 'Single-task & Single-domain' and 'Multi-task & Multi-domain' in Section 2, and also under both 'GNN and LLM as independent collaborative modules' and 'GNN-only' in Section 4. GraphFM is listed under both 'LLM-enhanced GNN' and 'GNN-only' in Section 4, and GraphProp appears under both 'Single-task & Multi-domain' in Section 2 and 'LLM-enhanced GNN' in Section 4. Since Section 8 claims that the framework 'systematically categorize[s]' the field, categories within a single perspective must be exclusive; these duplicate placements invalidate the classification claim as stated.
[§2, §4, §5, §6] The same method is cited inconsistently across sections. GOFA is cited as [43] in Section 2 and in the Section 5 method description, but appears as 'GOFA[78]' in Sections 4 and 6, where reference [78] is in fact AnyGraph, not GOFA. GraphProp is cited as [53] in Section 2, but reference [53] is GraphPrompt; GraphProp is later cited as [8] in Sections 4 and 6. These inconsistencies make it impossible for a reader to verify which method is being classified in each category.
[References [1]–[15]] Fifteen references, [1] through [15], are anonymous 'under review' ICLR 2024 submissions. These works are used as substantive entries in Sections 2, 4, and 6 and are described in detail in Section 5. Because their content cannot be checked, any category placement involving them is unverifiable, and duplicated entries such as GraphBridge[6] appearing in two categories cannot be resolved by reading the cited source. The survey should either remove these entries or clearly mark them as unverified and exclude them from the central taxonomy.
[§1, §2, §8] The survey claims to be comprehensive and systematic, but it does not state its literature search strategy, inclusion criteria, or method-selection protocol. The lists in Sections 2 and 4 are labeled 'main works' and 'representative research papers,' which is not the same as a systematic categorization. Without explicit selection criteria, the 'comprehensive' and 'foundational' claims in the abstract and Section 8 are not supportable.

minor comments (5)

[§5] The method description for WalkLM is headed 'WalkFM [68]' in the text, while the reference list and Section 6 use 'WalkLM'; the heading should be consistent.
[§5] The heading 'SimTEG [26]' should read 'SimTeG' to match the reference and the rest of the manuscript.
[Tables 3 and 4] There are typos in the tables: 'Tokoler' should be 'Tolokers' and 'conncetivity' should be 'connectivity.'
[§6] In Section 6, category (2), 'TAGA[57]' is inconsistent with Section 2 and the reference list, where TAGA is [87]; reference [57] is CIKM-KD.
[Tables 1–5] The dataset citations in the tables appear unreliable; for example, Cora is cited as [13] and [32] in Table 1, but those reference numbers correspond to anonymous OMOG and TAPE, not to the original Cora dataset sources.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the survey's taxonomy is descriptive, and its inconsistencies are accuracy issues rather than derivation-by-construction.

full rationale

The paper is a literature survey organized by a stipulated Data/Model/Task taxonomy. It performs no quantitative derivation, fits no parameters, and does not 'predict' any quantity from an input, so there is no derivation chain that could reduce to its own inputs by construction. The central contribution, the classification framework in Sections 2, 4, and 6, is a descriptive organizational scheme rather than a theorem or model output. The duplicated placement of methods such as GraphBridge, GOFA, GraphFM, and GraphProp across categories that the paper defines as distinct is a genuine reproducibility and accuracy weakness in the taxonomy, but it is an inconsistency, not circularity: the duplicate entries do not make the categorizing procedure equivalent to its own assumptions, nor do they constitute a fitted parameter renamed as a prediction. The reliance on fifteen anonymous under-review references is a verifiability and integrity concern, but not a circularity concern, because the survey cites those works as items being organized rather than as independent proof of a derived claim. No self-citation chain, imported uniqueness theorem, or ansatz smuggled in via citation appears. The honest finding is therefore no significant circularity.

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

No free parameters or invented entities. The survey's load-bearing assumptions are about the completeness and exclusivity of its taxonomy and the verifiability of its citations; both are internally contradicted.

assumptions (3)

domain assumption The three-pillar framework (Data, Model, Task) is a complete and non-overlapping organization of the GNN-LLM literature.
Stated in the abstract and Section 1 as the basis for a 'systematic' survey; no coverage or exclusion criteria are provided.
domain assumption Each method belongs to exactly one category within each perspective.
Required for a clean taxonomy; violated by GraphBridge appearing in both Single-task & Single-domain and Multi-task & Multi-domain in Section 2, and in both independent modules and GNN-only in Section 4.
ad hoc to paper References [1]-[15], listed as 'Anonymous' ICLR 2024 submissions under review, are real and correctly described.
These unverifiable entries are used as representative methods in Sections 2, 4, and 6; the reader cannot check their content.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks." pith.science (2026). https://pith.science/paper/M2W6VPUR

@misc{pith2026241212456,
  author       = {Pith},
  title        = {Pith review of: Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/M2W6VPUR}},
  note         = {Machine review of arXiv:2412.12456}
}

read the original abstract

With the increasing prevalence of cross-domain Text-Attributed Graph (TAG) Data (e.g., citation networks, recommendation systems, social networks, and ai4science), the integration of Graph Neural Networks (GNNs) and Large Language Models (LLMs) into a unified Model architecture (e.g., LLM as enhancer, LLM as collaborators, LLM as predictor) has emerged as a promising technological paradigm. The core of this new graph learning paradigm lies in the synergistic combination of GNNs' ability to capture complex structural relationships and LLMs' proficiency in understanding informative contexts from the rich textual descriptions of graphs. Therefore, we can leverage graph description texts with rich semantic context to fundamentally enhance Data quality, thereby improving the representational capacity of model-centric approaches in line with data-centric machine learning principles. By leveraging the strengths of these distinct neural network architectures, this integrated approach addresses a wide range of TAG-based Task (e.g., graph learning, graph reasoning, and graph question answering), particularly in complex industrial scenarios (e.g., supervised, few-shot, and zero-shot settings). In other words, we can treat text as a medium to enable cross-domain generalization of graph learning Model, allowing a single graph model to effectively handle the diversity of downstream graph-based Task across different data domains. This work serves as a foundational reference for researchers and practitioners looking to advance graph learning methodologies in the rapidly evolving landscape of LLM. We consistently maintain the related open-source materials at \url{https://github.com/xkLi-Allen/Awesome-GNN-in-LLMs-Papers}.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

A Query-Aware Multi-Path Knowledge Graph Fusion Approach for Enhancing Retrieval-Augmented Generation in Large Language Models
cs.IR 2025-07 conditional novelty 5.0 of 10

QMKGF builds multi-path knowledge graph subgraphs from LLM-extracted entities, fuses the highest-scoring subgraph with query-relevant triples, and expands the query to improve RAG answer quality.
Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities
eess.SP 2025-09 conditional novelty 4.0 of 10

AI can be used to generate interactive signal processing courseware, but the paper offers no evidence that students learn better from it.

Reference graph

Works this paper leans on

93 extracted references · 77 canonical work pages · cited by 2 Pith papers

[43]

Gofa: A generative o ne-for-all model for joint graph language modeling

Lecheng Kong, Jiarui Feng, Hao Liu, Chengsong Huang, Ji axin Huang, Yixin Chen, and Muhan Zhang. Gofa: A generative o ne-for-all model for joint graph language modeling. 2024

work page 2024
[78]

Anygraph: Graph foundatio n model in the wild

Lianghao Xia and Chao Huang. Anygraph: Graph foundatio n model in the wild. 2024

work page 2024
[1]

Curriculum GNN-LLM alignment for text-attr ibuted graphs

Anonymous. Curriculum GNN-LLM alignment for text-attr ibuted graphs. In Submitted to The Thirteenth International Conference on Le arning Representations, 2024. under review

2024
[15]

Towards graph foundation models: Learning generalities across graphs via task-trees

Anonymous. Towards graph foundation models: Learning generalities across graphs via task-trees. In Submitted to The Thirteenth International Conference on Learning Representations , 2024. under review

work page 2024
[40]

Can gnn be good adapter for ll ms? 2024

Xuanwen Huang, Kaiqiao Han, Yang Yang, Dezheng Bao, Qua njin Tao, Ziwei Chai, and Qi Zhu. Can gnn be good adapter for ll ms? 2024

work page 2024
[87]

Taga: Text-attributed graph self-supervised learning b y synergizing graph and text mutual transformations

Zheng Zhang, Yuntong Hu, Bo Pan, Chen Ling, and Liang Zha o. Taga: Text-attributed graph self-supervised learning b y synergizing graph and text mutual transformations. 2024

work page 2024
[57]

Distilling large language models for text-attributed graph learning

Bo Pan, Zheng Zhang, Yifei Zhang, Yuntong Hu, and Liang Z hao. Distilling large language models for text-attributed graph learning. 2024

work page 2024
[53]

Gr aphprompt: Unifying pre-training and downstream tasks for graph neural networks

Zemin Liu, Xingtong Yu, Yuan Fang, and Xinming Zhang. Gr aphprompt: Unifying pre-training and downstream tasks for graph neural networks. 2023

work page 2023
[8]

Graphprop: Training the graph foundation mo dels using graph properties

Anonymous. Graphprop: Training the graph foundation mo dels using graph properties. In Submitted to The Thirteenth International Conference on Learning Representations, 2024. under review

2024
[6]

Graphbridge: Towards arbitrary transfer le arning in GNNs

Anonymous. Graphbridge: Towards arbitrary transfer le arning in GNNs. In Submitted to The Thirteenth International Conference on Le arning Representations, 2024. under review

2024

Show all 93 references

[2]

DP-GPL: Diﬀerentially private graph prompt learning

Anonymous. DP-GPL: Diﬀerentially private graph prompt learning. In Submitted to The Thirteenth International Conference on Le arning Represen- tations, 2024. under review

2024
[3]

Edge prompt tuning for graph neural networks

Anonymous. Edge prompt tuning for graph neural networks . In Submitted to The Thirteenth International Conference on Le arning Representations,
[4]

GFSE: A foundational model for graph structu ral encoding

Anonymous. GFSE: A foundational model for graph structu ral encoding. In Submitted to The Thirteenth International Conference on Le arning Representations, 2024. under review

2024
[5]

GL-fusion: Rethinking the combination of gr aph neural network and large language model

Anonymous. GL-fusion: Rethinking the combination of gr aph neural network and large language model. In Submitted to The Thirteenth Interna- tional Conference on Learning Representations , 2024. under review

2024
[7]

GraphFM: A generalist graph transformer tha t learns transferable representations across diverse doma ins

Anonymous. GraphFM: A generalist graph transformer tha t learns transferable representations across diverse doma ins. In Submitted to The Thirteenth International Conference on Learning Represen tations, 2024. under review

2024
[9]

Large language models based graph convoluti on for text-attributed networks

Anonymous. Large language models based graph convoluti on for text-attributed networks. In Submitted to The Thirteenth International Conference on Learning Representations, 2024. under review

2024
[10]

Link prediction on text attributed graphs: A new benchmark and eﬃcient LM-nested GNN design

Anonymous. Link prediction on text attributed graphs: A new benchmark and eﬃcient LM-nested GNN design. In Submitted to The Thirteenth International Conference on Learning Representations , 2024. under review

2024
[11]

LLM as GNN: Graph vocabulary learning for gr aph foundation model

Anonymous. LLM as GNN: Graph vocabulary learning for gr aph foundation model. In Submitted to The Thirteenth International Conference on Learning Representations, 2024. under review

2024
[12]

Low-cost enhancer for text attributed grap h learning via graph alignment

Anonymous. Low-cost enhancer for text attributed grap h learning via graph alignment. In Submitted to The Thirteenth International Conference on Learning Representations, 2024. under review

2024
[13]

One model for one graph: A new perspective fo r pretraining with cross-domain graphs

Anonymous. One model for one graph: A new perspective fo r pretraining with cross-domain graphs. In Submitted to The Thirteenth International Conference on Learning Representations , 2024. under review

2024
[14]

Text attributed graph node classiﬁcation u sing sheaf neural networks and large language models

Anonymous. Text attributed graph node classiﬁcation u sing sheaf neural networks and large language models. In Submitted to The Thirteenth International Conference on Learning Representations , 2024. under review

2024
[16]

Lpnl: Scalable link prediction with large langu age models

Baolong Bi, Shenghua Liu, Yiwei Wang, Lingrui Mei, and X ueqi Cheng. Lpnl: Scalable link prediction with large langu age models. 2024

2024
[17]

Pro tein function prediction via graph kernels

Karsten M Borgwardt, Cheng Soon Ong, Stefan Schönauer, SVN Vishwanathan, Alex J Smola, and Hans-Peter Kriegel. Pro tein function prediction via graph kernels. Bioinformatics, 21(suppl_1):i47–i56, 2005

2005
[18]

Congrat: Self-supe rvised contrastive pre- training for joint graph and text embeddings

William Brannon, Wonjune Kang, Suyash Fulay, Hang Jian g, Brandon Roy, Deb Roy, and Jad Kabbara. Congrat: Self-supe rvised contrastive pre- training for joint graph and text embeddings. 2024

2024
[19]

Graphllm: Boosting grap h reasoning ability of large language model

Ziwei Chai, Tianjie Zhang, Liang Wu, Kaiqiao Han, Xiaoh ai Hu, Xuanwen Huang, and Yang Yang. Graphllm: Boosting grap h reasoning ability of large language model. 2023

2023
[20]

Llaga: Large language and graph assistant

Runjin Chen, Tong Zhao, Ajay Jaiswal, Neil Shah, and Zha ngyang Wang. Llaga: Large language and graph assistant. 202 4
[21]

Hight: Hierarchical graph tokenization for graph-language align- ment

Yongqiang Chen, Quanming Yao, Juzheng Zhang, James Che ng, and Yatao Bian. Hight: Hierarchical graph tokenization for graph-language align- ment. 2024

2024
[22]

Label-free node c lassiﬁcation on graphs with large language models (llms)

Zhikai Chen, Haitao Mao, Hongzhi Wen, Haoyu Han, Wei Jin , Haiyang Zhang, Hui Liu, and Jiliang Tang. Label-free node c lassiﬁcation on graphs with large language models (llms). 2024

2024
[23]

N ode feature extraction by self-supervised multi-scale neighborhood prediction

Eli Chien, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Y u, Jiong Zhang, Olgica Milenkovic, and Inderjit S Dhillon. N ode feature extraction by self-supervised multi-scale neighborhood prediction. 20 22
[24]

Structure-activ ity relationship of mutagenic aromatic and heteroaromatic nitro compounds

Asim Kumar Debnath, Rosa L Lopez de Compadre, Gargi Debn ath, Alan J Shusterman, and Corwin Hansch. Structure-activ ity relationship of mutagenic aromatic and heteroaromatic nitro compounds. co rrelation with molecular orbital energies and hydrophobic ity. Journal of medicinal...

1991
[25]

Distinguishing enzyme s tructures from non-enzymes without alignments

Paul D Dobson and Andrew J Doig. Distinguishing enzyme s tructures from non-enzymes without alignments. Journal of molecular biology , 330(4):771–783, 2003

2003
[26]

Simteg: A frustratingly simple approach improves textual graph learning

Keyu Duan, Qian Liu, Tat-Seng Chua, Shuicheng Yan, Wei Tsang Ooi, Qizhe Xie, and Junxian He. Simteg: A frustratingly simple approach improves textual graph learning. 2023

2023
[27]

Universal prompt tuning for graph neural networks

Taoran Fang, Yunchao Mercer Zhang, Yang Yang, Chunping Wang, and Lei CHEN. Universal prompt tuning for graph neural networks. In Thirty-seventh Conference on Neural Information Processin g Systems, 2023. Manuscript submitted to ACM Graph Learning in the Era of LLMs: A Survey f...

2023
[28]

Gaugl lm: Improving graph contrastive learning for text-attribu ted graphs with large language models

Yi Fang, Dongzhe Fan, Daochen Zha, and Qiaoyu Tan. Gaugl lm: Improving graph contrastive learning for text-attribu ted graphs with large language models. 2024

2024
[29]

Ta lk like a graph: Encoding graphs for large language models

Bahare Fatemi, Jonathan Halcrow, and Bryan Perozzi. Ta lk like a graph: Encoding graphs for large language models. 2 023
[30]

Learning word vectors for 157 la nguages

Edouard Grave, Piotr Bojanowski, Prakhar Gupta, Arman d Joulin, and Tomas Mikolov. Learning word vectors for 157 la nguages. arXiv preprint arXiv:1802.06893, 2018

2018 arXiv
[31]

Gpt4graph: Can large language models understand g raph structured data? an empirical evaluation and benchmarking

Jiayan Guo, Lun Du, Hengyu Liu, Mengyu Zhou, Xinyi He, an d Shi Han. Gpt4graph: Can large language models understand g raph structured data? an empirical evaluation and benchmarking. 2023

2023
[32]

Harnessing explanations: Llm -to-lm interpreter for enhanced text-attributed graph representation learning

Xiaoxin He, Xavier Bresson, Thomas Laurent, Adam Perol d, Yann LeCun, and Bryan Hooi. Harnessing explanations: Llm -to-lm interpreter for enhanced text-attributed graph representation learning. 2024

2024
[33]

Generalizing graph transformers across diverse g raphs and tasks via pre-training on industrial-scale data, 2024

Yufei He, Zhenyu Hou, Yukuo Cen, Feng He, Xu Cheng, and Br yan Hooi. Generalizing graph transformers across diverse g raphs and tasks via pre-training on industrial-scale data, 2024

2024
[34]

Unigraph : Learning a uniﬁed cross-domain foundation model for text- attributed graphs

Yufei He, Yuan Sui, Xiaoxin He, and Bryan Hooi. Unigraph : Learning a uniﬁed cross-domain foundation model for text- attributed graphs. 2024

2024
[35]

King, Stefan Kramer, and Ashwi n Srinivasan

Christoph Helma, Ross D. King, Stefan Kramer, and Ashwi n Srinivasan. The predictive toxicology challenge 2000–20 01. Bioinformatics, 17(1):107– 108, 2001

2000
[36]

Graphalign: Pretraining one graph neural network on multiple graphs via feature alignment

Zhenyu Hou, Haozhan Li, Yukuo Cen, Jie Tang, and Yuxiao D ong. Graphalign: Pretraining one graph neural network on multiple graphs via feature alignment. 2024

2024
[37]

Op en graph benchmark: Datasets for machine learning on graphs

Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, H ongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. Op en graph benchmark: Datasets for machine learning on graphs. Advances in Neural Information Processing Systems, NeurIPS , 2020

2020
[38]

Scalable and accurate graph reasoning with llm-bas ed multi-agents

Yuwei Hu, Runlin Lei, Xinyi Huang, Zhewei Wei, and Yongc hao Liu. Scalable and accurate graph reasoning with llm-bas ed multi-agents. 2024

2024
[39]

PRODIGY: Enabling i n-context learning over graphs

Qian Huang, Hongyu Ren, Peng Chen, Gregor Kržmanc, Dani el Zeng, Percy Liang, and Jure Leskovec. PRODIGY: Enabling i n-context learning over graphs. In Thirty-seventh Conference on Neural Information Processin g Systems, 2023

2023
[41]

Ragraph: A general retrieval-augmented graph learning framework

Xinke Jiang, Rihong Qiu, Yongxin Xu, Wentao Zhang, Yich en Zhu, Ruizhe Zhang, Yuchen Fang, Xu Chu, Junfeng Zhao, and Y asha Wang. Ragraph: A general retrieval-augmented graph learning framework. 2 024
[42]

Patton: Language model pretraining on text-rich networks

Bowen Jin, Wentao Zhang, Yu Zhang, Yu Meng, Xinyang Zhang, Qi Zhu, and Jiawei Han. Patton: Language model pretraining on text-rich networks. 2023

2023
[44]

Graphs over time: densiﬁcation laws, shrinking diameters a nd possible explanations

Jure Leskovec, Jon Kleinberg, and Christos Faloutsos. Graphs over time: densiﬁcation laws, shrinking diameters a nd possible explanations. In Proceedings of the ACM SIGKDD Conference on Knowledge Discov ery and Data Mining, KDD , 2005

2005
[45]

Snap datasets: Stanfor d large network dataset collection

Jure Leskovec and Andrej Krevl. Snap datasets: Stanfor d large network dataset collection. 2014

2014
[46]

Datasets: A community lib rary for natural language processing

Quentin Lhoest, Albert Villanova del Moral, Yacine Jer nite, Abhishek Thakur, Patrick von Platen, Suraj Patil, Jul ien Chaumond, Mariama Drame, Julien Plu, Lewis Tunstall, et al. Datasets: A community lib rary for natural language processing. arXiv preprint arXiv:2109.02846 , 2021

2021 arXiv
[47]

Finemo ltex: Towards ﬁne-grained molecular graph-text pre-train ing

Yibo Li, Yuan Fang, Mengmei Zhang, and Chuan Shi. Finemo ltex: Towards ﬁne-grained molecular graph-text pre-train ing. 2024

2024
[48]

Grenade: Graph- centric language model for self-supervised representatio n learning on text-attributed graphs

Yichuan Li, Kaize Ding, and Kyumin Lee. Grenade: Graph- centric language model for self-supervised representatio n learning on text-attributed graphs. 2023

2023
[49]

Zerog: Investigating cross-dataset zero-shot transfer ability in graphs

Yuhan Li, Peisong Wang, Zhixun Li, Jeﬀrey Xu Yu, and Jia L i. Zerog: Investigating cross-dataset zero-shot transfer ability in graphs. 2024

2024
[50]

Toloker Graph: Interaction of Crowd Annotators

Daniil Likhobaba, Nikita Pavlichenko, and Dmitry Usta lov. Toloker Graph: Interaction of Crowd Annotators. 2023

2023
[51]

Link predict ion on textual edge graphs, 2024

Chen Ling, Zhuofeng Li, Yuntong Hu, Zheng Zhang, Zhongy uan Liu, Shuang Zheng, Jian Pei, and Liang Zhao. Link predict ion on textual edge graphs, 2024

2024
[52]

One for all: Towards tra ining one graph model for all classiﬁcation tasks

Hao Liu, Jiarui Feng, Lecheng Kong, Ningyue Liang, Dach eng Tao, Yixin Chen, and Muhan Zhang. One for all: Towards tra ining one graph model for all classiﬁcation tasks. 2024

2024
[54]

Zheyuan Liu, Xiaoxin He, Yijun Tian, and Nitesh V. Chawl a. Can we soft prompt llms for graph learning tasks? In Companion Proceedings of the ACM Web Conference 2024, WWW ’24, page 481–484, New York, NY, USA, 2024. Association for Computing Machinery

2024
[55]

Ioannidis, Shen Wang, D a Zheng, Soji Adeshina, Jun Ma, Han Zhao, Christos Faloutsos , and George Karypis

Costas Mavromatis, Vassilis N. Ioannidis, Shen Wang, D a Zheng, Soji Adeshina, Jun Ma, Han Zhao, Christos Faloutsos , and George Karypis. Train your own gnn teacher: Graph-aware distillation on textual g raphs. 2023

2023
[56]

Tagexplainer: Narrating graph explanati ons for text-attributed graph learning models

Bo Pan, Zhen Xiong, Guanchen Wu, Zheng Zhang, Yifei Zhan g, and Liang Zhao. Tagexplainer: Narrating graph explanati ons for text-attributed graph learning models. 2024

2024
[58]

L et your graph do the talking: Encoding structured data for llms

Bryan Perozzi, Bahare Fatemi, Dustin Zelle, Anton Tsit sulin, Mehran Kazemi, Rami Al-Rfou, and Jonathan Halcrow. L et your graph do the talking: Encoding structured data for llms. 2024

2024
[59]

Disent angled representation learning with large language models for text-attributed graphs

Yijian Qin, Xin Wang, Ziwei Zhang, and Wenwu Zhu. Disent angled representation learning with large language models for text-attributed graphs. 2024. Manuscript submitted to ACM 26 Xunkai Li, Zhengyu Wu, Jiayi Wu, Hanwen Cui, Jishuo Jia, Ro nghua Li, and Guoren Wang

2024
[60]

Iam graph database repos itory for graph based pattern recognition and machine learn ing

Kaspar Riesen and Horst Bunke. Iam graph database repos itory for graph based pattern recognition and machine learn ing. In Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, SSPR & SPR 2008, Orlando, USA, December 4-6, 2008.Proceedi...

2008
[61]

Unleashing the potential of text-attributed graphs: Automatic relation decomposition via large language models

Hyunjin Seo, Taewon Kim, June Yong Yang, and Eunho Yang. Unleashing the potential of text-attributed graphs: Automatic relation decomposition via large language models. 2024

2024
[62]

Pitfalls of graph neural netw ork evaluation

Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojch evski, and Stephan Günnemann. Pitfalls of graph neural netw ork evaluation. arXiv preprint arXiv:1811.05868, 2018

2018 arXiv
[63]

A multi-view mixture-of-experts based on language and grap hs for molecular properties prediction

Victor Yukio Shirasuna, Eduardo Soares, Emilio Vital Brazil, Karen Fiorella Aquino Gutierrez, Renato Cerqueira,Seiji Takeda, and Akihiro Kishimoto. A multi-view mixture-of-experts based on language and grap hs for molecular properties prediction. In ICML 2024 AI for Science Wo...

2024
[64]

Gppt: Graph pre-training and prompt tuning to generali ze graph neural networks

Mingchen Sun, Kaixiong Zhou, Xin He, Ying Wang, and Xin W ang. Gppt: Graph pre-training and prompt tuning to generali ze graph neural networks. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Di scovery and Data Mining , KDD ’22, page 1717–1727, New York, NY, USA...

2022
[65]

All in one: Multi-task prompting for graph neural networks

Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu, and Jihong Guan . All in one: Multi-task prompting for graph neural networks . 2023

2023
[66]

Spline-ﬁtting with a genetic algorithm: A method for develo ping classiﬁcation structure- activity relationships

Jeﬀrey J Sutherland, Lee A O’brien, and Donald F Weaver. Spline-ﬁtting with a genetic algorithm: A method for develo ping classiﬁcation structure- activity relationships. Journal of chemical information and computer sciences , 43(6):1906–1915, 2003

1906
[67]

Musegraph: Graph-oriented instruction tuning of large language models for generic graph mining

Yanchao Tan, Hang Lv, Xinyi Huang, Jiawei Zhang, Shipin g Wang, and Carl Yang. Musegraph: Graph-oriented instruction tuning of large language models for generic graph mining. 2024

2024
[68]

Walklm: A uniform language model ﬁne-tuning framework for attributed graph embedding

Yanchao Tan, Zihao Zhou, Hang Lv, Weiming Liu, and Carl Y ang. Walklm: A uniform language model ﬁne-tuning framework for attributed graph embedding. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. H ardt, and S. Levine, editors, Advances in Neural Information Processing Syst...
[69]

Graphgpt: Graph instructi on tuning for large language models

Jiabin Tang, Yuhao Yang, Wei Wei, Lei Shi, Lixin Su, Suqi Cheng, Dawei Yin, and Chao Huang. Graphgpt: Graph instructi on tuning for large language models. 2024

2024
[70]

Higpt: Heterogeneous graph language m odel

Jiabin Tang, Yuhao Yang, Wei Wei, Lei Shi, Long Xia, Dawe i Yin, and Chao Huang. Higpt: Heterogeneous graph language m odel. 2024

2024
[71]

Compariso n of descriptor spaces for chemical compound retrieval and c lassiﬁcation

Nikil Wale, Ian A Watson, and George Karypis. Compariso n of descriptor spaces for chemical compound retrieval and c lassiﬁcation. Knowledge and Information Systems , 14:347–375, 2008

2008
[72]

Can language models solve graph problems in natural language? 2024

Heng Wang, Shangbin Feng, Tianxing He, Zhaoxuan Tan, Xi aochuang Han, and Yulia Tsvetkov. Can language models solve graph problems in natural language? 2024

2024
[73]

Instructgraph: Boosting large language m odels via graph-centric instruction tuning and preference alignment

Jianing Wang, Junda Wu, Yupeng Hou, Yao Liu, Ming Gao, an d Julian McAuley. Instructgraph: Boosting large language m odels via graph-centric instruction tuning and preference alignment. 2024

2024
[74]

Towards graph foundation mode ls: The perspective of zero-shot reasoning on knowledge gra phs

Kai Wang and Siqiang Luo. Towards graph foundation mode ls: The perspective of zero-shot reasoning on knowledge gra phs. 2024

2024
[75]

Microsoft academic grap h: When experts are not enough

Kuansan Wang, Zhihong Shen, Chiyuan Huang, Chieh-Han W u, Yuxiao Dong, and Anshul Kanakia. Microsoft academic grap h: When experts are not enough. Quantitative Science Studies, 1(1):396–413, 2020

2020
[76]

Learning graph quantized tokenizers for transformers

Limei Wang, Kaveh Hassani, Si Zhang, Dongqi Fu, Baichua n Yuan, Weilin Cong, Zhigang Hua, Hao Wu, Ning Yao, and Bo Long . Learning graph quantized tokenizers for transformers. 2024

2024
[77]

Augmenting low-resource text classiﬁcation with graph-grounded pre-training and promp ting

Zhihao Wen and Yuan Fang. Augmenting low-resource text classiﬁcation with graph-grounded pre-training and promp ting. In Proceedings of the 46th International ACM SIGIR Conference on Research and Dev elopment in Information Retrieval , SIGIR ’23, page 506–516. ACM, July 2023

2023
[79]

Opengraph: Towar ds open graph foundation models

Lianghao Xia, Ben Kao, and Chao Huang. Opengraph: Towar ds open graph foundation models. 2024

2024
[80]

Ioann idis, Xiang Song, Qing Ping, Sheng Wang, Carl Yang, Yi Xu, Bel inda Zeng, and Trishul Chilimbi

Han Xie, Da Zheng, Jun Ma, Houyu Zhang, Vassilis N. Ioann idis, Xiang Song, Qing Ping, Sheng Wang, Carl Yang, Yi Xu, Bel inda Zeng, and Trishul Chilimbi. Graph-aware language model pre-training on a lar ge graph corpus can help multiple graph applications. 2023

2023
[81]

Language models are graph learners

Zhe Xu, Kaveh Hassani, Si Zhang, Hanqing Zeng, Michihir o Yasunaga, Limei Wang, Dongqi Fu, Ning Yao, Bo Long, and Hanghang Tong. Language models are graph learners. 2024

2024
[82]

Deep graph kernel s

Pinar Yanardag and SVN Vishwanathan. Deep graph kernel s. In Proceedings of the ACM SIGKDD Conference on Knowledge Discov ery and Data Mining, KDD, 2015

2015
[83]

Cohen, and Ruslan Salakhutdino v

Zhilin Yang, William W. Cohen, and Ruslan Salakhutdino v. Revisiting semi-supervised learning with graph embeddings. In International Conference on Machine Learning, ICML , 2016

2016
[84]

Language is all a graph needs

Ruosong Ye, Caiqi Zhang, Runhui Wang, Shuyuan Xu, and Yo ngfeng Zhang. Language is all a graph needs. In Yvette Graham and Matthew Purver, editors, Findings of the Association for Computational Linguistics : EACL 2024, pages 1955–1973, St. Julian’s, Malta, March 2024. Associa ...

2024
[85]

M ultigprompt for multi-task pre-training and prompting on g raphs

Xingtong Yu, Chang Zhou, Yuan Fang, and Xinming Zhang. M ultigprompt for multi-task pre-training and prompting on g raphs. 2024

2024
[86]

Graphtra nslator: Aligning graph model to large language model for open-ended tasks

Mengmei Zhang, Mingwei Sun, Peng Wang, Shen Fan, Yanhu M o, Xiaoxiao Xu, Hong Liu, Cheng Yang, and Chuan Shi. Graphtra nslator: Aligning graph model to large language model for open-ended tasks. 20 24
[88]

Graphany: A foundat ion model for node classiﬁcation on any graph

Jianan Zhao, Hesham Mostafa, Mikhail Galkin, Michael B ronstein, Zhaocheng Zhu, and Jian Tang. Graphany: A foundat ion model for node classiﬁcation on any graph. 2024. Manuscript submitted to ACM Graph Learning in the Era of LLMs: A Survey from the Perspecti ve of Data, Models...

2024
[89]

Learning on large-scale text-at tributed graphs via variational inference

Jianan Zhao, Meng Qu, Chaozhuo Li, Hao Yan, Qian Liu, Rui Li, Xing Xie, and Jian Tang. Learning on large-scale text-at tributed graphs via variational inference. 2023

2023
[90]

Graphtext: Gra ph reasoning in text space

Jianan Zhao, Le Zhuo, Yikang Shen, Meng Qu, Kai Liu, Mich ael Bronstein, Zhaocheng Zhu, and Jian Tang. Graphtext: Gra ph reasoning in text space. 2023

2023
[91]

Ioannidis, Danai Kout ra, and Christos Faloutsos

Jing Zhu, Xiang Song, Vassilis N. Ioannidis, Danai Kout ra, and Christos Faloutsos. Touchup-g: Improving feature r epresentation through graph- centric ﬁnetuning. 2023

2023
[92]

Eﬃci ent tuning and inference for large language models on textua l graphs

Yun Zhu, Yaoke Wang, Haizhou Shi, and Siliang Tang. Eﬃci ent tuning and inference for large language models on textua l graphs. 2024

2024
[93]

Pre training language models with text-attributed heterogene ous graphs

Tao Zou, Le Yu, Yifei Huang, Leilei Sun, and Bowen Du. Pre training language models with text-attributed heterogene ous graphs. 2023. Manuscript submitted to ACM

2023

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

[1] [43]

Gofa: A generative o ne-for-all model for joint graph language modeling

Lecheng Kong, Jiarui Feng, Hao Liu, Chengsong Huang, Ji axin Huang, Yixin Chen, and Muhan Zhang. Gofa: A generative o ne-for-all model for joint graph language modeling. 2024

work page 2024

[2] [78]

Anygraph: Graph foundatio n model in the wild

Lianghao Xia and Chao Huang. Anygraph: Graph foundatio n model in the wild. 2024

work page 2024

[3] [1]

Curriculum GNN-LLM alignment for text-attr ibuted graphs

Anonymous. Curriculum GNN-LLM alignment for text-attr ibuted graphs. In Submitted to The Thirteenth International Conference on Le arning Representations, 2024. under review

2024

[4] [15]

Towards graph foundation models: Learning generalities across graphs via task-trees

Anonymous. Towards graph foundation models: Learning generalities across graphs via task-trees. In Submitted to The Thirteenth International Conference on Learning Representations , 2024. under review

work page 2024

[5] [40]

Can gnn be good adapter for ll ms? 2024

Xuanwen Huang, Kaiqiao Han, Yang Yang, Dezheng Bao, Qua njin Tao, Ziwei Chai, and Qi Zhu. Can gnn be good adapter for ll ms? 2024

work page 2024

[6] [87]

Taga: Text-attributed graph self-supervised learning b y synergizing graph and text mutual transformations

Zheng Zhang, Yuntong Hu, Bo Pan, Chen Ling, and Liang Zha o. Taga: Text-attributed graph self-supervised learning b y synergizing graph and text mutual transformations. 2024

work page 2024

[7] [57]

Distilling large language models for text-attributed graph learning

Bo Pan, Zheng Zhang, Yifei Zhang, Yuntong Hu, and Liang Z hao. Distilling large language models for text-attributed graph learning. 2024

work page 2024

[8] [53]

Gr aphprompt: Unifying pre-training and downstream tasks for graph neural networks

Zemin Liu, Xingtong Yu, Yuan Fang, and Xinming Zhang. Gr aphprompt: Unifying pre-training and downstream tasks for graph neural networks. 2023

work page 2023

[9] [8]

Graphprop: Training the graph foundation mo dels using graph properties

Anonymous. Graphprop: Training the graph foundation mo dels using graph properties. In Submitted to The Thirteenth International Conference on Learning Representations, 2024. under review

2024

[10] [6]

Graphbridge: Towards arbitrary transfer le arning in GNNs

Anonymous. Graphbridge: Towards arbitrary transfer le arning in GNNs. In Submitted to The Thirteenth International Conference on Le arning Representations, 2024. under review

2024