Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2405.05784.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
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 2026-08-12T18:53:48.184553Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T18:12:39.871652Z
0 of 0 outbound references displayed
External citation measurements
No source-named external measurement is stored.
No outbound reference observations are available for this paper version.
Observation 6fe65f29-6a87-43bd-9ee7-c91d36ec0b04 · inbound
Stealing Training Graphs from Graph Neural Networks Link Stealing Attacks Against Inductive Graph Neural Networks
Reference 67
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a4874f45-6361-4166-99ec-74ac8124b825 · inbound
Sub-optimal Learning in Meta-Classifier Attacks: A Study of Membership Inference on Differentially Private Location Aggregates Link Stealing Attacks Against Inductive Graph Neural Networks
Reference 34
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3e6d8192-7608-46df-a698-24196fde3cfc · inbound
GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Link Stealing Attacks Against Inductive Graph Neural Networks
Reference 46
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f29e630b-828d-4a43-948e-e3628bea76c8 · inbound
A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives Link Stealing Attacks Against Inductive Graph Neural Networks
Reference 227
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.