Paper Citation Record · LEDGER

Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2309.16595.

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

pith.paper-citation-record.v1
2309.16595 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:02:12.078202Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

3
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 23b158b9-821d-4551-b8bc-6fdae05d43bb · inbound

LLM Online Spatial-temporal Signal Reconstruction Under Noise cites this paper.

LLM Online Spatial-temporal Signal Reconstruction Under Noise Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T14:02:12.078202Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c42616f-627c-4fe0-b820-22afc4d71b72 · inbound

GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design cites this paper.

GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T14:01:38.362345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23649a85-c76f-4e80-accd-862231a8f2c8 · inbound

Court of LLMs: Evidence-Augmented Generation via Multi-LLM Collaboration for Text-Attributed Graph Anomaly Detection cites this paper.

Court of LLMs: Evidence-Augmented Generation via Multi-LLM Collaboration for Text-Attributed Graph Anomaly Detection Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T10:15:17.163112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46760b49-d394-477c-ac39-be8f33ce79de · inbound

A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM cites this paper.

A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-10T18:00:42.030811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bb7ea5c6-648f-4247-be41-fb054f95d61b · inbound

STK-Adapter: Incorporating Evolving Graph and Event Chain for Temporal Knowledge Graph Extrapolation cites this paper.

STK-Adapter: Incorporating Evolving Graph and Event Chain for Temporal Knowledge Graph Extrapolation Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?

Reference 76

Resolution: verified exact; arxiv_id, observed 2026-05-11T12:51:06.900332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e11bdbb3-c4e5-4771-93f2-38f0541c8992 · 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 Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-12T11:01:31.283146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9491358e-1ade-4f0a-b80e-a603a1438d60 · 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 Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-30T11:44:38.460061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e6059fcb-f531-4b0b-96f2-ca77a71bd99e · inbound

Knowledge Graph-Enhanced Zero-Shot Topic Classification: A Multi-Strategy Comparative Study cites this paper.

Knowledge Graph-Enhanced Zero-Shot Topic Classification: A Multi-Strategy Comparative Study Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T07:53:13.690027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 02a9b2ef-d3f3-469f-aee0-621d8a8d2ecc · 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 Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-07-03T09:07:47.679300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9ab1d4f0-a822-4725-9a61-0de5e0530b21 · inbound

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

GLIP: Graph and LLM Joint Pretraining for Graph-Level Tasks Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-06-30T07:34:21.256045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d3bf79a8-7db6-4ca6-951e-e61c33a232f3 · 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 Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?

Reference 37

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T18:47:17.205108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 082a1f8b-8b65-4199-bcf7-0ccbf7968ed5 · inbound

Agentic Graph Token Reasoning cites this paper.

Agentic Graph Token Reasoning Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T00:44:14.144217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.