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Paper Citation Record · LEDGER

r-GAT: Relational Graph Attention Network for Multi-Relational Graphs

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2109.05922.

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

pith.paper-citation-record.v1
2109.05922 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:50:58.778458Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T05:50:59.079943Z

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 aef009cf-c2ee-4a2e-bf08-a2fed70f54e7 · inbound

Graph Neural Networks in Modern AI-aided Drug Discovery cites this paper.

Graph Neural Networks in Modern AI-aided Drug Discovery r-GAT: Relational Graph Attention Network for Multi-Relational Graphs

Reference 83

Resolution
verified exact
local_arxiv, observed 2026-08-07T05:50:59.083657Z

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=2026-08-07T05:50:58.778458Z digest=sha256:cb82754750d1d05ec6d2af1061ae6e09c5e1119e6770751410dbe0cffd4363b5

Observation a84c03eb-8ccf-4d62-afe2-faa240d4cb1f · inbound

Minos: Systematically Classifying Performance and Power Characteristics of GPU Workloads on HPC Clusters cites this paper.

Minos: Systematically Classifying Performance and Power Characteristics of GPU Workloads on HPC Clusters r-GAT: Relational Graph Attention Network for Multi-Relational Graphs

Reference 16

Resolution
unresolved
no resolver link, observed 2026-07-13T12:55:27.280416Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T12:55:27.280416Z digest=sha256:2ac5d38b2cdb60c880e83a1def9c004212baf233bc5452a03c76eb5e12ab7ae9