Pith. sign in

Paper Citation Record · LEDGER

Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2408.08685.

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

pith.paper-citation-record.v1
2408.08685 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T13:29:52.157856Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T08:46:07.754457Z

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 dfee304a-4a96-442e-ac52-77f96e837a12 · inbound

When LLM Agents Meet Graph Optimization: An Automated Data Quality Improvement Approach cites this paper.

When LLM Agents Meet Graph Optimization: An Automated Data Quality Improvement Approach Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?

Reference 56

Resolution
verified exact
arxiv_id, observed 2026-05-18T08:46:07.757046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-18T08:45:51.992431Z digest=sha256:7566b07699746406125da0ddbce3c25e0dcebb6dc6656202c06256755c977c02

Observation 5fe6c8e6-fbad-4f6e-8d76-a59f736834c8 · inbound

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation cites this paper.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-01T13:29:52.157856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T13:29:52.157856Z digest=sha256:b07cede16386df5ef36c034dbba053761213e2fa911114242785fd47ba6136b7