Paper Citation Record · LEDGER

PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2402.04284.

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

pith.paper-citation-record.v1
2402.04284 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:10:33.255894Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T17:45:46.223848Z

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 68f63d28-e099-4bc5-a9b8-a882fec76c6b · inbound

Graph Retention Networks for Dynamic Graphs cites this paper.

Graph Retention Networks for Dynamic Graphs PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-23T17:45:46.226853Z

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 6c117719-2af6-4e0e-ac4b-2ec443e9568e · inbound

TIDFormer: Exploiting Temporal and Interactive Dynamics Makes A Great Dynamic Graph Transformer cites this paper.

TIDFormer: Exploiting Temporal and Interactive Dynamics Makes A Great Dynamic Graph Transformer PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T12:10:33.255894Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 591511be-d439-43b3-a75d-da6d750b8d88 · 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 PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:03.489658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92e6498c-eba6-4686-967f-a02e7d57c9c2 · 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 PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-05T18:41:19.900502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.