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Paper Citation Record · LEDGER

GrassNet: State Space Model Meets Graph Neural Network

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2408.08583.

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

pith.paper-citation-record.v1
2408.08583 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 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T04:51:05.726924Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T14:32:46.207514Z

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 6f239b86-ca1f-492c-9d42-e7f882288383 · inbound

Exploring Graph Mamba: A Comprehensive Survey on State-Space Models for Graph Learning cites this paper.

Exploring Graph Mamba: A Comprehensive Survey on State-Space Models for Graph Learning GrassNet: State Space Model Meets Graph Neural Network

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-11T04:51:05.726924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T04:51:05.726924Z digest=sha256:8c6397eb6f46540932643fb872282ceb4397a8f00eddc3e9e8302bfab3961e94

Observation 4bcdad8f-bb7e-4d8f-8960-26feb7b32fef · inbound

Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling cites this paper.

Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling GrassNet: State Space Model Meets Graph Neural Network

Reference 121

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:32:46.268572Z

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.

source=pdf_text observed=2026-08-07T14:32:45.578015Z digest=sha256:d17fa7ff21c8e02deed2a9f080d679734b6ab4ca776cf498707c4f9f41c7c097