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Paper Citation Record · LEDGER

Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2302.01018.

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

pith.paper-citation-record.v1
2302.01018 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 2026-08-07T00:47:58.626456Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T07:59:40.558376Z

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 468cb2d0-9c17-479b-a402-14c525122c71 · inbound

Large Scalable Cross-Domain Graph Neural Networks for Personalized Notification at LinkedIn cites this paper.

Large Scalable Cross-Domain Graph Neural Networks for Personalized Notification at LinkedIn Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-07T00:47:58.626456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:47:58.626456Z digest=sha256:95c8b77c242e478af78a4d26f558e42c191706b2c8fe7fc53fc305149889dcc6

Observation aadd76e7-991f-4887-8307-02407ebc50b3 · inbound

DiT-SGCR: Directed Temporal Structural Representation with Global-Cluster Awareness for Ethereum Malicious Account Detection cites this paper.

DiT-SGCR: Directed Temporal Structural Representation with Global-Cluster Awareness for Ethereum Malicious Account Detection Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-06T23:04:03.762195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:04:03.762195Z digest=sha256:5cd854067c1eb5e2a10f1540c69ef77569a0c51b0039f2a220e1cb5daedf70bd

Observation b2d90753-558c-45e3-bc42-a36c5d6f646a · inbound

Boosting Team Modeling through Tempo-Relational Representation Learning cites this paper.

Boosting Team Modeling through Tempo-Relational Representation Learning Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities

Reference 78

Resolution
verified exact
arxiv_id, observed 2026-05-19T03:47:02.497706Z

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.

source=pdf_text observed=2026-05-19T03:43:01.418431Z digest=sha256:d6301fc9ef1b31b0e76495177f206be556425b41a981027fa1f8da4eff446998

Observation fa571feb-aae5-4e06-b880-3923de47f78d · inbound

When Speed meets Accuracy: an Efficient and Effective Graph Model for Temporal Link Prediction cites this paper.

When Speed meets Accuracy: an Efficient and Effective Graph Model for Temporal Link Prediction Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-06T16:23:01.315459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:23:01.315459Z digest=sha256:59d7ca0bb4265445c488f6262817cb6175d6135845665e0991f0f97e41be7896

Observation 80712f83-ebea-43d6-aaf9-ea1ef2b46306 · inbound

DevoTG: Temporal Graph Neural Networks for Modeling C. elegans Developmental Connectomics cites this paper.

DevoTG: Temporal Graph Neural Networks for Modeling C. elegans Developmental Connectomics Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-07-04T07:59:40.559630Z

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.

source=arxiv_source observed=2026-06-26T12:17:36.104393Z digest=sha256:f4870c69acb533ad1c599734127c448c610d4c36dd5ba54e7bece99cfd1e1488