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Paper Citation Record · LEDGER

Understanding Heterophily for Graph Neural Networks

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2401.09125.

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

pith.paper-citation-record.v1
2401.09125 v2

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 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T16:00:51.673184Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T05:57:22.379145Z

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 15612f42-b4b7-4387-8b13-ff1e14f5d3e5 · inbound

Dynamic Triangulation-Based Graph Rewiring for Graph Neural Networks cites this paper.

Dynamic Triangulation-Based Graph Rewiring for Graph Neural Networks Understanding Heterophily for Graph Neural Networks

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-05T16:00:51.673184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T16:00:51.673184Z digest=sha256:6e034d37931e7dfffc2db058d5389ff09cf0b1d7b71d51120bc4cb487b116986

Observation 1d1308a0-77db-4524-912c-1331c5ed545d · inbound

Hierarchical Multi-Scale Graph Neural Networks: Scalable Heterophilous Learning with Oversmoothing and Oversquashing Mitigation cites this paper.

Hierarchical Multi-Scale Graph Neural Networks: Scalable Heterophilous Learning with Oversmoothing and Oversquashing Mitigation Understanding Heterophily for Graph Neural Networks

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-13T05:57:22.380738Z

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.

source=pdf_text observed=2026-05-13T05:57:17.440810Z digest=sha256:60e2ab069db493a6cd58360526b42b336b684e18bf01edb637b484e4c85c15cf