Paper Citation Record · LEDGER

A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2405.00476.

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

pith.paper-citation-record.v1
2405.00476 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:14:09.978270Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T17:45:46.202919Z

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 df9c4a47-273b-43d3-99ea-9c1fe165e854 · inbound

Graph Retention Networks for Dynamic Graphs cites this paper.

Graph Retention Networks for Dynamic Graphs A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-23T17:45:46.206520Z

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 4052379e-e2d0-44c9-a332-2fbc99ca9be0 · inbound

TMetaNet: Topological Meta-Learning Framework for Dynamic Link Prediction cites this paper.

TMetaNet: Topological Meta-Learning Framework for Dynamic Link Prediction A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T12:14:09.978270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7924e0a7-eb23-4cbf-a63c-8532f3a49f6c · 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 A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T00:47:58.587465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a2bc3e0-0e9e-4134-bfa9-f57a404f68da · inbound

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams cites this paper.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:02.087994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17751001-5330-436f-91fc-146a7c545030 · inbound

T-GRAB: A Synthetic Diagnostic Benchmark for Learning on Temporal Graphs cites this paper.

T-GRAB: A Synthetic Diagnostic Benchmark for Learning on Temporal Graphs A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T17:46:34.054494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22b5c8a8-91c9-448e-9c7c-418dcc6e78f3 · inbound

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure cites this paper.

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T14:52:34.185889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6fdd5035-a3a0-419d-9110-14ebd2e52ff0 · inbound

THGFM: Dual-Branch Temporal Heterogeneous Graph Fusion Model cites this paper.

THGFM: Dual-Branch Temporal Heterogeneous Graph Fusion Model A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T09:58:37.903210Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.