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Paper Citation Record · LEDGER

Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2107.06865.

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

pith.paper-citation-record.v1
2107.06865 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 2026-08-05T06:32:48.257954+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:39:44.212594Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T22:39:44.520585Z

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 f9b47bd0-97ad-4802-9cef-4d1cf3018fb3 · inbound

Geometry-Aware Spiking Graph Neural Network cites this paper.

Geometry-Aware Spiking Graph Neural Network Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning

Reference 2021

Resolution
metadata mismatch
local_arxiv, observed 2026-08-05T22:39:44.611171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-08-05T22:39:44.212594Z digest=sha256:b80613994c365661fa5b4a0723097d696ee379fac7c389b572146e1ef4c32d1b

Observation c0c7867e-c229-4669-bf1d-4402dc755373 · inbound

Scalable and Efficient Joint Spiking Embedding Predictive Architecture for Large-Scale Dynamic Graphs cites this paper.

Scalable and Efficient Joint Spiking Embedding Predictive Architecture for Large-Scale Dynamic Graphs Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-01T15:32:34.971005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T15:32:34.971005Z digest=sha256:a40b6967e4168e34e05da39652f1a4873d78968533f261334f357c62b4af17b0