Pith. sign in

Paper Citation Record · LEDGER

LTE4G: Long-Tail Experts for Graph Neural Networks

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2208.10205.

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

pith.paper-citation-record.v1
2208.10205 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 1 of 1 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T13:29:56.395987Z

measured 0 of 1 external citation measurements

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

Source: cited_works

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 c563a9f4-4b01-4904-983d-e5af90ddc90d · inbound

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation cites this paper.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation LTE4G: Long-Tail Experts for Graph Neural Networks

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-01T13:29:56.395987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T13:29:56.395987Z digest=sha256:10b78499792a3cb6050c2f53f8270149456917eba470cb8e105f0b38bef0b93d