Paper Citation Record · LEDGER

Relational Graph Attention Networks

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:1904.05811.

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

pith.paper-citation-record.v1
1904.05811 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T23:33:30.802691Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-06-29T11:43:23.268890Z

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 cc24e965-6a01-43cd-9cad-697f7755b290 · inbound

How Attentive are Graph Attention Networks? cites this paper.

How Attentive are Graph Attention Networks? Relational Graph Attention Networks

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-17T02:33:38.733013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3b9c2f98-4661-4f29-a39f-65f211044391 · inbound

No Need to Train Your RDB Foundation Model cites this paper.

No Need to Train Your RDB Foundation Model Relational Graph Attention Networks

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-02T23:33:30.802691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6498dd2d-63ca-4e5a-9244-7ea92d9b1ea1 · inbound

LUMINA: Foundation Models for Topology Transferable ACOPF cites this paper.

LUMINA: Foundation Models for Topology Transferable ACOPF Relational Graph Attention Networks

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-15T16:40:10.496478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9429e092-d03d-420c-b83a-bb826ee2fde3 · inbound

AFGNN: API Misuse Detection using Graph Neural Networks and Clustering cites this paper.

AFGNN: API Misuse Detection using Graph Neural Networks and Clustering Relational Graph Attention Networks

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:25:52.665965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 59d3f466-cd7c-41ae-ad3a-b913fa993d74 · inbound

FOCAL-Attention for Heterogeneous Multi-Label Prediction cites this paper.

FOCAL-Attention for Heterogeneous Multi-Label Prediction Relational Graph Attention Networks

Reference 42

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T12:46:21.327933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0a3c8714-df39-4b33-a41e-585fb361ee14 · inbound

Attention-based graph neural networks: a survey cites this paper.

Attention-based graph neural networks: a survey Relational Graph Attention Networks

Reference 42

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T08:26:24.958736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 327b03f8-19fe-467c-bb74-fb8b072a55b5 · inbound

All Circuits Lead to Rome: Rethinking Functional Anisotropy in Circuit and Sheaf Discovery for LLMs cites this paper.

All Circuits Lead to Rome: Rethinking Functional Anisotropy in Circuit and Sheaf Discovery for LLMs Relational Graph Attention Networks

Reference 126

Resolution: metadata mismatch; local_arxiv, observed 2026-05-14T20:52:57.544401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3880cb5c-d7c2-407c-ac01-ac7b4d4ffa5e · inbound

Leveraging Graph Structure in Seq2Seq Models for Knowledge Graph Link Prediction cites this paper.

Leveraging Graph Structure in Seq2Seq Models for Knowledge Graph Link Prediction Relational Graph Attention Networks

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-05-20T10:48:13.029083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 93c9b85a-3013-4ce0-8a85-6a5911da3d57 · inbound

Confident Learning-based Network for Detecting Bug-Inducing Commits on SZZ with Noisy Labels cites this paper.

Confident Learning-based Network for Detecting Bug-Inducing Commits on SZZ with Noisy Labels Relational Graph Attention Networks

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-06-29T11:43:23.270230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.