Paper Citation Record · LEDGER

Rethinking the Expressive Power of GNNs via Graph Biconnectivity

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2301.09505.

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

pith.paper-citation-record.v1
2301.09505 v3

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 2026-08-22T06:32:14.747728+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:25:38.224788Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T17:15:25.405238Z

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 c8aad00b-e56a-44cc-9bf6-fba901cf6c07 · inbound

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles cites this paper.

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles Rethinking the Expressive Power of GNNs via Graph Biconnectivity

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-09T04:16:22.384732Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e03777e6-f55b-4795-bc23-f9ec9a008216 · inbound

Computing and Learning on Combinatorial Data cites this paper.

Computing and Learning on Combinatorial Data Rethinking the Expressive Power of GNNs via Graph Biconnectivity

Reference 290

Resolution: unresolved; no resolver link, observed 2026-08-08T20:29:47.571605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba3b5f9c-2209-44d8-a0e1-84167ed65faa · inbound

Relational Deep Learning: Challenges, Foundations and Next-Generation Architectures cites this paper.

Relational Deep Learning: Challenges, Foundations and Next-Generation Architectures Rethinking the Expressive Power of GNNs via Graph Biconnectivity

Reference 104

Resolution: unresolved; no resolver link, observed 2026-08-15T19:25:38.224788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ff7cf5f-aa9a-4e6c-ba7f-281e8c901ca1 · 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 Rethinking the Expressive Power of GNNs via Graph Biconnectivity

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-05T18:41:21.350824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1664b630-08f6-4a35-b919-7fbe0ccec08e · inbound

Learning from Historical Activations in Graph Neural Networks cites this paper.

Learning from Historical Activations in Graph Neural Networks Rethinking the Expressive Power of GNNs via Graph Biconnectivity

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-21T17:15:25.408581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.