Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2408.08685.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T05:29:38.346112Z
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 98371fc0-dd1e-4710-998c-7baf31809062 · inbound
Trustworthy GNNs with LLMs: A Systematic Review and Taxonomy cites this paper.
Trustworthy GNNs with LLMs: A Systematic Review and Taxonomy Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T05:29:38.346112Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 684b970f-1123-4ca1-bc7d-c25d6e372ce3 · inbound
Poster: Enhancing GNN Robustness for Network Intrusion Detection via Agent-based Analysis cites this paper.
Poster: Enhancing GNN Robustness for Network Intrusion Detection via Agent-based Analysis Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:47:09.164255Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6ad276cf-93a6-466d-973d-c70cf9b2cb0a · inbound
REAL-IoT: Characterizing GNN Intrusion Detection Robustness under Practical Adversarial Attack cites this paper.
REAL-IoT: Characterizing GNN Intrusion Detection Robustness under Practical Adversarial Attack Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?
Reference 66
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T17:28:40.619204Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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 2026-08-08T06:32:00.761636+00:00.
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.