Paper Citation Record · LEDGER

A Survey on The Expressive Power of Graph Neural Networks

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2003.04078.

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

pith.paper-citation-record.v1
2003.04078 v4

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 2026-08-08T06:32:00.761636+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T18:41:19.411539Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T05:30:23.137102Z

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 6ecaf42a-6322-41ea-ba60-9ff351ae2456 · inbound

Grothendieck Graph Neural Networks Framework: An Algebraic Platform for Crafting Topology-Aware GNNs cites this paper.

Grothendieck Graph Neural Networks Framework: An Algebraic Platform for Crafting Topology-Aware GNNs A Survey on The Expressive Power of Graph Neural Networks

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-23T07:22:42.988232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 211178c3-98fa-4453-8751-2f0bdae94586 · inbound

Universal Spin Models are Universal Approximators in Machine Learning cites this paper.

Universal Spin Models are Universal Approximators in Machine Learning A Survey on The Expressive Power of Graph Neural Networks

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-19T06:02:07.924118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation aa344fdd-ff60-4d8d-bd08-31daf1898e43 · inbound

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks cites this paper.

On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks A Survey on The Expressive Power of Graph Neural Networks

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-05T18:41:19.411539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab0ef5d8-6c37-49db-9386-687baf47bf78 · inbound

S$^3$GNN: Efficient Global Mixing and Local Message Passing for Long-Range Graph Learning cites this paper.

S$^3$GNN: Efficient Global Mixing and Local Message Passing for Long-Range Graph Learning A Survey on The Expressive Power of Graph Neural Networks

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-25T05:30:23.139538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9d1aad3a-bfc5-468e-8184-9456dfe89b00 · inbound

Same Graph Cross-Task Transfer in GNNs: Protocols and Predictors cites this paper.

Same Graph Cross-Task Transfer in GNNs: Protocols and Predictors A Survey on The Expressive Power of Graph Neural Networks

Reference 47

Resolution: unresolved; no resolver link, observed 2026-07-31T04:39:46.890608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.