Paper Citation Record · LEDGER

When Do LLMs Help With Node Classification? A Comprehensive Analysis

As of 22 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 7 inbound Pith citation observations for arXiv:2502.00829.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.00829 v2

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T17:37:13.231402Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-14T13:51:14.141383Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-03T11:08:03.216419Z

Reference resolution

27 of 27 outbound references displayed

verified exact0
verified fuzzy9
unresolved17
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation acb8d571-7518-43a2-91bd-0caf5f5d6db9 · outbound

This paper cites an unresolved cited work.

When Do LLMs Help With Node Classification? A Comprehensive Analysis Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-09T17:37:13.394684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation baed8db4-97d5-46ca-8ebe-b3491d62934a · outbound

This paper cites From Anchors to Answers: A Novel Node Tokenizer for Integrating Graph Structure into Large Language Models.

When Do LLMs Help With Node Classification? A Comprehensive Analysis From Anchors to Answers: A Novel Node Tokenizer for Integrating Graph Structure into Large Language Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-09T17:37:13.153089Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f08a64f-c250-4843-bd6f-e73f7fc38c57 · outbound

This paper cites Mistral 7B.

When Do LLMs Help With Node Classification? A Comprehensive Analysis Mistral 7B

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T17:37:13.156910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18adbee5-e9ba-4264-8f57-5536a33f1bf5 · outbound

This paper cites Large Language Models on Graphs: A Comprehensive Survey.

When Do LLMs Help With Node Classification? A Comprehensive Analysis Large Language Models on Graphs: A Comprehensive Survey

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-09T17:37:13.160658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff70d993-d7da-40c5-806d-e9608fc372cf · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

When Do LLMs Help With Node Classification? A Comprehensive Analysis RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-09T17:37:13.168075Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f179666-b68d-462a-bdca-691644839208 · outbound

This paper cites Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks.

When Do LLMs Help With Node Classification? A Comprehensive Analysis Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-09T17:37:13.171864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1589984-9e8d-4edf-9c62-e8125659088e · outbound

This paper cites and Gurevych, I.

When Do LLMs Help With Node Classification? A Comprehensive Analysis and Gurevych, I

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:37:13.483407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e9cc77db-6cc8-4b70-95b1-15456d7fca46 · outbound

This paper cites Text Embeddings by Weakly-Supervised Contrastive Pre-training.

When Do LLMs Help With Node Classification? A Comprehensive Analysis Text Embeddings by Weakly-Supervised Contrastive Pre-training

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-09T17:37:13.183486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0dd7e48-d90d-4df2-8727-3038b113e4f3 · outbound

This paper cites GraphText: Graph Reasoning in Text Space.

When Do LLMs Help With Node Classification? A Comprehensive Analysis GraphText: Graph Reasoning in Text Space

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T17:37:13.186581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 229de3f4-71fb-43cb-89d3-791419e75e39 · outbound

This paper cites Graphclip: Enhancing trans- ferability in graph foundation models for text-attributed graphs.

When Do LLMs Help With Node Classification? A Comprehensive Analysis Graphclip: Enhancing trans- ferability in graph foundation models for text-attributed graphs

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:37:13.473945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fe49c470-26b5-463c-8447-c59688734dbc · outbound

This paper cites Related Works and Discussion In this section, we present a comprehensive taxonomy of node classification methods, ranging from classic approaches to those leveraging LLMs.

When Do LLMs Help With Node Classification? A Comprehensive Analysis Related Works and Discussion In this section, we present a comprehensive taxonomy of node classification methods, ranging from classic approaches to those leveraging LLMs

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:37:13.464607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7ca6067b-b54f-4724-911c-c3ab8112d54f · outbound

This paper cites an unresolved cited work.

When Do LLMs Help With Node Classification? A Comprehensive Analysis Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-09T17:37:13.445794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b3d69951-cae6-447b-a9c5-36a8cf01d7e9 · outbound

This paper cites an unresolved cited work.

When Do LLMs Help With Node Classification? A Comprehensive Analysis Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-09T17:37:13.437768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 85b9140f-5123-464d-9a45-6ac9a752f836 · outbound

This paper cites w. Neighbor.

When Do LLMs Help With Node Classification? A Comprehensive Analysis w. Neighbor

Reference 20

Resolution: malformed identifier; raw_fallback, observed 2026-08-09T17:37:13.429444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7fab4188-7a31-4175-88d2-234d34aed853 · outbound

This paper cites Photo”) and Ele-Computer (abbreviated as “Computer.

When Do LLMs Help With Node Classification? A Comprehensive Analysis Photo”) and Ele-Computer (abbreviated as “Computer

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:37:13.421486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation bd5a6dac-863e-4a3d-ae18-75a5dfe3c6e9 · outbound

This paper cites an unresolved cited work.

When Do LLMs Help With Node Classification? A Comprehensive Analysis Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-09T17:37:13.385596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation df83e3d8-58a8-4096-8a4d-f4b72dd4f007 · outbound

This paper cites These GNNs can be customized with various layers and embedding dimensions.

When Do LLMs Help With Node Classification? A Comprehensive Analysis These GNNs can be customized with various layers and embedding dimensions

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:37:13.376468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6dd736c7-7052-4a58-90e1-ebdf15d4a910 · outbound

This paper cites Additionally, LLMNodeBed allows users to specify and invoke any LM or LLM of their choice, providing flexibility for diverse research needs.

When Do LLMs Help With Node Classification? A Comprehensive Analysis Additionally, LLMNodeBed allows users to specify and invoke any LM or LLM of their choice, providing flexibility for diverse research needs

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:37:13.365668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation faec15b3-c836-4aae-8f8c-8762b923f039 · outbound

This paper cites Random Walk.

When Do LLMs Help With Node Classification? A Comprehensive Analysis Random Walk

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:37:13.403493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 97711c66-ba8a-4dcc-a325-3a80d1376cb9 · outbound

This paper cites GraphLLM: Boosting Graph Reasoning Ability of Large Language Model.

When Do LLMs Help With Node Classification? A Comprehensive Analysis GraphLLM: Boosting Graph Reasoning Ability of Large Language Model

Reference 2006

Resolution: unresolved; no resolver link, observed 2026-08-09T17:37:13.136468Z

Source-reported events for the cited work

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Observation 55083c98-5d06-4b02-8742-77a9769751f9 · outbound

This paper cites an unresolved cited work.

When Do LLMs Help With Node Classification? A Comprehensive Analysis Unresolved cited work

Reference 2016

Resolution: unresolved; raw_fallback, observed 2026-08-09T17:37:13.412986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ef25df3c-6503-4f97-94d3-80799ea395eb · outbound

This paper cites Can llms ef- fectively leverage graph structural information through prompts, and why? Trans.

When Do LLMs Help With Node Classification? A Comprehensive Analysis Can llms ef- fectively leverage graph structural information through prompts, and why? Trans

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:37:13.503595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 548fa33d-09f0-4194-8352-aed348c3eddf · outbound

This paper cites GraphGPT: Graph Instruction Tuning for Large Language Models.

When Do LLMs Help With Node Classification? A Comprehensive Analysis GraphGPT: Graph Instruction Tuning for Large Language Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-09T17:37:13.179478Z

Source-reported events for the cited work

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Observation 2e516365-0448-44f5-b6e2-f600ce3b433e · outbound

This paper cites an unresolved cited work.

When Do LLMs Help With Node Classification? A Comprehensive Analysis Unresolved cited work

Reference 2022

Resolution: unresolved; raw_fallback, observed 2026-08-09T17:37:13.454903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d55a4f74-bf39-470c-8e5c-987de9964768 · outbound

This paper cites Can gnn be good adapter for llms? Proceedings of the ACM on Web Conference 2024,.

When Do LLMs Help With Node Classification? A Comprehensive Analysis Can gnn be good adapter for llms? Proceedings of the ACM on Web Conference 2024,

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:37:13.493501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation efda623d-d62b-4ba0-b55f-926979476298 · outbound

This paper cites The Llama 3 Herd of Models.

When Do LLMs Help With Node Classification? A Comprehensive Analysis The Llama 3 Herd of Models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-09T17:37:13.141096Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d1c987d-c6e4-4ac8-96af-562b658f2f76 · outbound

This paper cites A Survey of Graph Meets Large Language Model: Progress and Future Directions.

When Do LLMs Help With Node Classification? A Comprehensive Analysis A Survey of Graph Meets Large Language Model: Progress and Future Directions

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-09T17:37:13.164321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation c9862f82-4f52-4b79-8e17-fb6c82347f76 · inbound

When Structure Doesn't Help: LLMs Do Not Read Text-Attributed Graphs as Effectively as We Expected cites this paper.

When Structure Doesn't Help: LLMs Do Not Read Text-Attributed Graphs as Effectively as We Expected When Do LLMs Help With Node Classification? A Comprehensive Analysis

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:20:11.834826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 654da98b-3272-41be-8762-2da4c3ffeb20 · inbound

HopRank: Self-Supervised LLM Preference-Tuning on Graphs for Few-Shot Node Classification cites this paper.

HopRank: Self-Supervised LLM Preference-Tuning on Graphs for Few-Shot Node Classification When Do LLMs Help With Node Classification? A Comprehensive Analysis

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-10T06:06:18.705426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 56c0e332-d129-4717-b67d-2eb3d643e89e · inbound

G2LoRA: Gradient Orthogonal Low-Rank Adaptation Framework for Graph Continual Learning on Text-Attributed Graphs cites this paper.

G2LoRA: Gradient Orthogonal Low-Rank Adaptation Framework for Graph Continual Learning on Text-Attributed Graphs When Do LLMs Help With Node Classification? A Comprehensive Analysis

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-07-01T22:26:17.561987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation bfc5b639-4eb2-4541-a540-7ef8872bd789 · inbound

Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teaching cites this paper.

Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teaching When Do LLMs Help With Node Classification? A Comprehensive Analysis

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T09:07:47.656553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 465015af-eab2-4007-9e10-b5b61d8c7408 · inbound

GraspLLM: Towards Zero-Shot Generalization on Text-Attributed Graphs with LLMs cites this paper.

GraspLLM: Towards Zero-Shot Generalization on Text-Attributed Graphs with LLMs When Do LLMs Help With Node Classification? A Comprehensive Analysis

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T11:08:03.217877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation df68b919-dcc1-4c5f-958f-70144d1b881f · inbound

GLIP: Graph and LLM Joint Pretraining for Graph-Level Tasks cites this paper.

GLIP: Graph and LLM Joint Pretraining for Graph-Level Tasks When Do LLMs Help With Node Classification? A Comprehensive Analysis

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-06-30T07:34:21.264420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 438d75a9-db51-4e6d-89d6-35bfc02323e2 · inbound

UNIT: Unleash Large Language Models Potential for Graph Continual Learning cites this paper.

UNIT: Unleash Large Language Models Potential for Graph Continual Learning When Do LLMs Help With Node Classification? A Comprehensive Analysis

Reference 32

Resolution: unresolved; no resolver link, observed 2026-07-14T13:51:14.141383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.