Paper Citation Record · LEDGER

GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 27 inbound Pith citation observations for arXiv:2305.15066.

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

pith.paper-citation-record.v1
2305.15066 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 27 of 27 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:58:33.715755Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T11:08:03.251020Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 867ed0d5-575c-4e79-ab85-fc08b261ad15 · inbound

Retrieval-Augmented Generation with Graphs (GraphRAG) cites this paper.

Retrieval-Augmented Generation with Graphs (GraphRAG) GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 130

Resolution: verified exact; arxiv_id, observed 2026-05-18T04:33:39.497071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cc156cd8-2b45-4309-acea-3e7ef449e412 · inbound

Walk&Retrieve: Simple Yet Effective Zero-shot Retrieval-Augmented Generation via Knowledge Graph Walks cites this paper.

Walk&Retrieve: Simple Yet Effective Zero-shot Retrieval-Augmented Generation via Knowledge Graph Walks GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:33.715755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1f25686-1b7d-4287-b5c7-8b1b8b41fa98 · inbound

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment cites this paper.

Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:41.854092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49d91962-8336-4372-af1f-2f056543d0d7 · inbound

Quantizing Text-attributed Graphs for Semantic-Structural Integration cites this paper.

Quantizing Text-attributed Graphs for Semantic-Structural Integration GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T15:51:12.742377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f5995b2-3644-43c7-a4f3-6caffb4d0839 · inbound

LLMs Between the Nodes: Community Discovery Beyond Vectors cites this paper.

LLMs Between the Nodes: Community Discovery Beyond Vectors GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T11:59:51.505005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 021cfc70-eb34-4fc8-a095-b15603878552 · inbound

Harnessing Adaptive Topology Representations for Zero-Shot Graph Question Answering cites this paper.

Harnessing Adaptive Topology Representations for Zero-Shot Graph Question Answering GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T22:55:20.818433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 509b6074-a527-4953-bb94-67080019734b · inbound

Capabilities of GPT-5 on Multimodal Medical Reasoning cites this paper.

Capabilities of GPT-5 on Multimodal Medical Reasoning GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T21:38:45.140675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30b97378-ed9f-4ce8-af4b-2ba8ac7852a9 · inbound

CS-Agent: LLM-based Community Search via Dual-agent Collaboration cites this paper.

CS-Agent: LLM-based Community Search via Dual-agent Collaboration GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T21:02:44.531211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation facdd556-dfff-414f-a621-9398f5792c5f · inbound

G-reasoner: Foundation Models for Unified Reasoning over Graph-structured Knowledge cites this paper.

G-reasoner: Foundation Models for Unified Reasoning over Graph-structured Knowledge GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:26:24.967403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8cabf8ad-9412-4dce-a515-54f2dcd11e50 · inbound

Deep sequence models tend to memorize geometrically; it is unclear why cites this paper.

Deep sequence models tend to memorize geometrically; it is unclear why GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-05-21T20:40:36.340161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c9217ba4-81a1-40ed-9cc8-893038202b43 · inbound

Both Topology and Text Matter: Revisiting LLM-guided Out-of-Distribution Detection on Text-attributed Graphs cites this paper.

Both Topology and Text Matter: Revisiting LLM-guided Out-of-Distribution Detection on Text-attributed Graphs GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T00:07:40.048367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8a01620-911c-4293-a9a4-72e7dc1917b1 · inbound

Generalization Boundaries of Fine-Tuned Small Language Models for Graph Structural Inference cites this paper.

Generalization Boundaries of Fine-Tuned Small Language Models for Graph Structural Inference GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-10T05:41:02.063011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c74620e8-49be-4bf5-a60e-fb6a669685f9 · inbound

Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding cites this paper.

Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-12T11:01:31.336100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5fc4c5b6-4eda-4a57-a263-80ee36b4e725 · inbound

GraphDC: A Divide-and-Conquer Multi-Agent System for Scalable Graph Algorithm Reasoning cites this paper.

GraphDC: A Divide-and-Conquer Multi-Agent System for Scalable Graph Algorithm Reasoning GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-11T01:45:52.264769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1b9dc0f1-2ee2-431e-b2af-0a49c6738cc8 · inbound

A Unified Graph Language Model for Multi-Domain Multi-Task Graph Alignment Instruction Tuning cites this paper.

A Unified Graph Language Model for Multi-Domain Multi-Task Graph Alignment Instruction Tuning GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:42:25.986609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation aad7cf5f-02d9-4a18-8607-a302d316243c · inbound

Linking Extreme Discourse to Structural Polarization in Signed Interaction Networks cites this paper.

Linking Extreme Discourse to Structural Polarization in Signed Interaction Networks GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 35

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T19:17:49.962068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 597a1fa7-f5b7-4eeb-851d-03a78c644d96 · inbound

TERGAD: Structure-Aware Text-Enhanced Representations for Graph Anomaly Detection cites this paper.

TERGAD: Structure-Aware Text-Enhanced Representations for Graph Anomaly Detection GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-20T06:48:05.677373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ef3883e0-e8f0-4437-b840-57ea9db2a516 · inbound

Clustering as Reasoning: A $k$-Means Interpretation of Chain-of-Thought Graph Learning cites this paper.

Clustering as Reasoning: A $k$-Means Interpretation of Chain-of-Thought Graph Learning GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-06-30T11:44:38.454386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 743370b8-93e6-4ac6-a4cc-baf00cbc6400 · inbound

Are Large Language Models Suitable for Graph Computation? Progress and Prospects cites this paper.

Are Large Language Models Suitable for Graph Computation? Progress and Prospects GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 183

Resolution: verified exact; arxiv_id, observed 2026-07-02T17:07:12.277702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d4eb20d5-5ba5-4be6-8a34-05379075b824 · inbound

GraphInfer-Bench: Benchmarking LLM's Inference Capability on Graphs cites this paper.

GraphInfer-Bench: Benchmarking LLM's Inference Capability on Graphs GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-03T08:57:47.508254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ed8032b6-8a67-4498-b831-a8c8474f9e42 · 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 GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-07-03T09:07:47.669784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 774c9948-e782-45b9-9d61-a6c367c0b787 · 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 GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-03T11:08:03.252473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8e1c198f-5082-43ba-8f47-8b2490381e0b · inbound

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

GLIP: Graph and LLM Joint Pretraining for Graph-Level Tasks GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-06-30T07:34:21.256523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 81ae04e9-09bf-4bed-a183-51d6c083f4fa · inbound

FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning cites this paper.

FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 172

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T09:45:40.619922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4b62596a-deac-4082-b187-8208fb54630f · inbound

AGE: Adaptive-masking for Graph Embedding in Graph Retrieval-Augmented Generation cites this paper.

AGE: Adaptive-masking for Graph Embedding in Graph Retrieval-Augmented Generation GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 26

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T18:47:16.673423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5d6bfa6f-b36b-40a0-b4df-6362c315b5bd · inbound

Agentic Graph Token Reasoning cites this paper.

Agentic Graph Token Reasoning GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T00:44:14.124054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f054a222-f859-41be-a318-2cde90f44b7e · inbound

GABench: A Comprehensive Benchmark for Evaluating LLM Agents on Graph Analysis Tasks cites this paper.

GABench: A Comprehensive Benchmark for Evaluating LLM Agents on Graph Analysis Tasks GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T22:55:33.882180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.