Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Trustworthy Graph Neural Networks: Aspects, Methods and Trends
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2205.07424.
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 2026-08-08T05:29:38.339147Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-30T21:55:05.383761Z
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 27475f30-36f0-4b83-aa06-042849e733d9 · inbound
Trustworthy GNNs with LLMs: A Systematic Review and Taxonomy cites this paper.
Trustworthy GNNs with LLMs: A Systematic Review and Taxonomy Trustworthy Graph Neural Networks: Aspects, Methods and Trends
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T05:29:38.339147Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9c246a62-be5c-4c04-ba48-335698877855 · inbound
Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach cites this paper.
Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Trustworthy Graph Neural Networks: Aspects, Methods and Trends
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T00:42:11.024361Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 42582e2a-f09a-4416-abd4-6c1b04413b63 · inbound
Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses cites this paper.
Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses Trustworthy Graph Neural Networks: Aspects, Methods and Trends
Reference 74
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T15:39:55.543085Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7af68be4-5ab5-4697-bd2e-7a0d07acd2e4 · inbound
GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It? cites this paper.
GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It? Trustworthy Graph Neural Networks: Aspects, Methods and Trends
Reference 36
- Resolution
- verified exact
- arxiv_id, observed 2026-05-14T19:29:23.821814Z
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 e6a49544-991e-41c9-8c5d-4673a9451a12 · inbound
GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It? cites this paper.
GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It? Trustworthy Graph Neural Networks: Aspects, Methods and Trends
Reference 35
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T21:55:05.385263Z
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.