Pith. sign in

Paper Citation Record · LEDGER

What Do GNNs Actually Learn? Towards Understanding their Representations

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2304.10851.

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

pith.paper-citation-record.v1
2304.10851 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:29:42.845714Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T07:55:31.677731Z

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 c0238e04-9da7-4fad-aae2-d14c9113adba · inbound

Positional Encoding meets Persistent Homology on Graphs cites this paper.

Positional Encoding meets Persistent Homology on Graphs What Do GNNs Actually Learn? Towards Understanding their Representations

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-07T10:29:42.845714Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:29:42.845714Z digest=sha256:60b057c0908d01cf31d2f6b02b9e2ca75499314c0e4f676f02020df2f789fde9

Observation d1351f11-f8fd-4dff-9ecb-9fb08f5ad844 · inbound

TACENR: Task-Agnostic Contrastive Explanations for Node Representations cites this paper.

TACENR: Task-Agnostic Contrastive Explanations for Node Representations What Do GNNs Actually Learn? Towards Understanding their Representations

Reference 21

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T03:29:21.743194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-10T03:27:15.874345Z digest=sha256:1d7c2469638ff6ecd78c827abaefafd0de2f55bdf9785121a29946fc21aeab54

Observation d97eb607-c421-42ac-bca9-2303e59269a3 · inbound

Aitchison Embeddings for Learning Compositional Graph Representations cites this paper.

Aitchison Embeddings for Learning Compositional Graph Representations What Do GNNs Actually Learn? Towards Understanding their Representations

Reference 12

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T15:41:29.578710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-09T19:28:00.793223Z digest=sha256:941b59b565b6634f291c0c07777c185e664cfc20c278e5325d24b157bc30e468

Observation 34f9a98d-73b5-460f-b224-9e119821ca00 · inbound

Aitchison Embeddings for Learning Compositional Graph Representations cites this paper.

Aitchison Embeddings for Learning Compositional Graph Representations What Do GNNs Actually Learn? Towards Understanding their Representations

Reference 12

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T07:55:31.679550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-07-01T07:43:22.946036Z digest=sha256:f86cbc533f2929aeced327986dce790bf670caad13ee5cf32757d7ff6d6793ed

Observation 736aca33-a5b9-4f51-a11c-79881c018cc4 · inbound

Rank Is Not Capacity: Spectral Occupancy for Latent Graph Models cites this paper.

Rank Is Not Capacity: Spectral Occupancy for Latent Graph Models What Do GNNs Actually Learn? Towards Understanding their Representations

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-05-13T06:27:24.671835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-13T06:24:35.029767Z digest=sha256:610282eef41a9367e2d71529c8281c0abfae00f5ef9ec3bb74b6eb4687e456a5