Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Talos: A More Effective and Efficient Adversarial Defense for GNN Models Based on the Global Homophily of Graphs
As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.03833.
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 2026-08-12T06:34:41.77262+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T20:30:56.116476Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-25T05:10:22.222855Z
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 0b3d411f-dfde-4b5d-91e6-c5452b1a83f3 · inbound
REGE: A Method for Incorporating Uncertainty in Graph Embeddings cites this paper.
REGE: A Method for Incorporating Uncertainty in Graph Embeddings Talos: A More Effective and Efficient Adversarial Defense for GNN Models Based on the Global Homophily of Graphs
Reference 60
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T20:30:56.116476Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e8804f09-5155-4eba-8dbf-31447058f131 · inbound
Self-supervised Adversarial Purification for Graph Neural Networks cites this paper.
Self-supervised Adversarial Purification for Graph Neural Networks Talos: A More Effective and Efficient Adversarial Defense for GNN Models Based on the Global Homophily of Graphs
Reference 26
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T05:10:22.225427Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.