Paper Citation Record · LEDGER

Global Attention Improves Graph Networks Generalization

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2006.07846.

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

pith.paper-citation-record.v1
2006.07846 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 2026-08-09T06:31:02.800959+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T19:59:58.816339Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T12:46:23.295848Z

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 5aacce06-ce6b-4c57-84c6-fb6b05cae555 · inbound

On the Effectiveness of Random Weights in Graph Neural Networks cites this paper.

On the Effectiveness of Random Weights in Graph Neural Networks Global Attention Improves Graph Networks Generalization

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-09T19:59:58.816339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 942eac43-9b57-45e5-84b0-004a1cf6ea1a · inbound

Adaptive Canonicalization with Application to Invariant Anisotropic Geometric Networks cites this paper.

Adaptive Canonicalization with Application to Invariant Anisotropic Geometric Networks Global Attention Improves Graph Networks Generalization

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-18T12:46:23.300839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5c9aec46-6d22-4bc6-bccc-a09bb3634865 · inbound

Towards Systematic Generalization for Power Grid Optimization Problems cites this paper.

Towards Systematic Generalization for Power Grid Optimization Problems Global Attention Improves Graph Networks Generalization

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:21:08.538057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f97761bf-e5e1-4ba3-a628-87d054c84277 · inbound

Universality and Approximation Rates of Graph Neural Networks with Random Features cites this paper.

Universality and Approximation Rates of Graph Neural Networks with Random Features Global Attention Improves Graph Networks Generalization

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-30T23:40:06.752628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.