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Paper Citation Record · LEDGER

Deep Generative Graph Distribution Learning for Synthetic Power Grids

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1901.09674.

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

pith.paper-citation-record.v1
1901.09674 v3

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 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T21:14:05.185515Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T10:36:35.821575Z

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 f67cfa18-8013-42b2-a49d-0372008ad564 · inbound

A Metric for the Balance of Information in Graph Learning cites this paper.

A Metric for the Balance of Information in Graph Learning Deep Generative Graph Distribution Learning for Synthetic Power Grids

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-09T21:14:05.185515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T21:14:05.185515Z digest=sha256:6bd3f7b77dd14ad2cc53f86c4ca50cd0bb4d4ac56d3e4d00a9788be00aee5a07

Observation 20635cc0-5f15-45ee-8714-ae1f2301abd3 · inbound

Operationally Feasible Synthetic Power-Grid Scenarios via Learning the AC-Operable Joint Distribution cites this paper.

Operationally Feasible Synthetic Power-Grid Scenarios via Learning the AC-Operable Joint Distribution Deep Generative Graph Distribution Learning for Synthetic Power Grids

Reference 19

Resolution
verified exact
local_arxiv, observed 2026-08-05T10:36:35.827461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T10:36:35.629733Z digest=sha256:9325a2d66516df3c7352185542c6858f5496c93b81ed1f3139ce64788ef5c2df