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Paper Citation Record · LEDGER

Structure-Preference Enabled Graph Embedding Generation under Differential Privacy

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2501.03451.

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

pith.paper-citation-record.v1
2501.03451 v1

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 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:26:20.938452Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T06:16:28.064256Z

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 40f61ae3-65f6-45d4-a6e6-825b589bfe89 · inbound

Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization cites this paper.

Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Structure-Preference Enabled Graph Embedding Generation under Differential Privacy

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-07T00:26:20.933649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:26:20.933649Z digest=sha256:2f8336623e899092300d589ed88dc2a6e0c9b5d81123a4d6e849e955961c6baa

Observation b34df0ed-f01a-4782-92ef-8c3c108d98fe · inbound

Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization cites this paper.

Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization Structure-Preference Enabled Graph Embedding Generation under Differential Privacy

Reference 49

Resolution
verified exact
local_arxiv, observed 2026-08-07T00:26:21.016694Z

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.

source=pdf_text observed=2026-08-07T00:26:20.938452Z digest=sha256:cc784ab805cdcccb1141dda211a9ed7b7fdea4e54b7134087f9d0418641e8ffc