Paper Citation Record · LEDGER

What Can We Learn from State Space Models for Machine Learning on Graphs?

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2406.05815.

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

pith.paper-citation-record.v1
2406.05815 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:32:40.070170Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T22:13:30.581867Z

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 c158162d-542c-41f6-b483-399126a4077c · inbound

A Survey of Mamba cites this paper.

A Survey of Mamba What Can We Learn from State Space Models for Machine Learning on Graphs?

Reference 83

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:13:30.583377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5c18872c-3152-4cf0-ad0d-fea373d40301 · 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 What Can We Learn from State Space Models for Machine Learning on Graphs?

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T14:32:40.070170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d918f2fd-4185-4c6b-b802-9c6e880418a2 · inbound

Rivaling Transformers: Multi-Scale Structured State-Space Mixtures for Agentic 6G O-RAN cites this paper.

Rivaling Transformers: Multi-Scale Structured State-Space Mixtures for Agentic 6G O-RAN What Can We Learn from State Space Models for Machine Learning on Graphs?

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T11:25:33.648056Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e45168b-71f6-48d9-acab-54e7a87e01c5 · inbound

Benchmarking Sheaf Neural Networks for Inductive Tasks cites this paper.

Benchmarking Sheaf Neural Networks for Inductive Tasks What Can We Learn from State Space Models for Machine Learning on Graphs?

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T04:52:32.160373Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.