Paper Citation Record · LEDGER

edGNN: a Simple and Powerful GNN for Directed Labeled Graphs

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1904.08745.

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

pith.paper-citation-record.v1
1904.08745 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 2026-08-13T06:32:02.005865+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T10:46:14.421975Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

11
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 507e9190-8125-4344-be69-739d1bf56676 · inbound

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations cites this paper.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations edGNN: a Simple and Powerful GNN for Directed Labeled Graphs

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T05:43:50.209457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11e61ab8-e89a-4ec0-9e1b-420a2631ff5b · inbound

Towards Data-centric Machine Learning on Directed Graphs: a Survey cites this paper.

Towards Data-centric Machine Learning on Directed Graphs: a Survey edGNN: a Simple and Powerful GNN for Directed Labeled Graphs

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-12T10:46:14.421975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c762f7d5-a3a9-4421-a038-71d49ff1e1cc · inbound

Extracting Money Laundering Transactions from Quasi-Temporal Graph Representation cites this paper.

Extracting Money Laundering Transactions from Quasi-Temporal Graph Representation edGNN: a Simple and Powerful GNN for Directed Labeled Graphs

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-13T20:53:15.027571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9a85a0d4-4a47-4e54-a005-64a2068917df · inbound

Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks cites this paper.

Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks edGNN: a Simple and Powerful GNN for Directed Labeled Graphs

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-06-29T23:14:02.130019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.