Paper Citation Record · LEDGER

Learning on Graphs with Large Language Models(LLMs): A Deep Dive into Model Robustness

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2407.12068.

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

pith.paper-citation-record.v1
2407.12068 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T05:29:38.253612Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T11:01:31.300577Z

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 fa7a1c90-0270-4b58-a76a-42f3af4ae5de · inbound

Trustworthy GNNs with LLMs: A Systematic Review and Taxonomy cites this paper.

Trustworthy GNNs with LLMs: A Systematic Review and Taxonomy Learning on Graphs with Large Language Models(LLMs): A Deep Dive into Model Robustness

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-08T05:29:38.253612Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be5b19ce-0aae-4e2a-b0e6-ba8aeebbc2b0 · inbound

TrustGLM: Evaluating the Robustness of GraphLLMs Against Prompt, Text, and Structure Attacks cites this paper.

TrustGLM: Evaluating the Robustness of GraphLLMs Against Prompt, Text, and Structure Attacks Learning on Graphs with Large Language Models(LLMs): A Deep Dive into Model Robustness

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T04:06:59.956264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77dd4d27-3b43-4a21-9113-62438765568b · inbound

Navigating the Black Box: Leveraging LLMs for Effective Text-Level Graph Injection Attacks cites this paper.

Navigating the Black Box: Leveraging LLMs for Effective Text-Level Graph Injection Attacks Learning on Graphs with Large Language Models(LLMs): A Deep Dive into Model Robustness

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T00:41:10.041480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0324f08-594d-4a92-aa56-a61d77c9e02f · 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 Learning on Graphs with Large Language Models(LLMs): A Deep Dive into Model Robustness

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-12T11:01:31.302905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.