Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Edge Directionality Improves Learning on Heterophilic Graphs
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2305.10498.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T13:19:10.948749Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
22
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 7f12add5-756f-4757-82f5-f0dcc288e398 · inbound
Flow Matters: Directional and Expressive GNNs for Heterophilic Graphs cites this paper.
Flow Matters: Directional and Expressive GNNs for Heterophilic Graphs Edge Directionality Improves Learning on Heterophilic Graphs
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T13:19:10.948749Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8163cc99-6a7b-4fe1-9376-5b466593d5e8 · inbound
Neural Networks With Dense Weights Are Not Universal Approximators cites this paper.
Neural Networks With Dense Weights Are Not Universal Approximators Edge Directionality Improves Learning on Heterophilic Graphs
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T06:07:25.495475Z
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 960802b2-6439-4cd8-91db-c7f753ccbe34 · inbound
Neural Networks With Dense Weights Are Not Universal Approximators cites this paper.
Neural Networks With Dense Weights Are Not Universal Approximators Edge Directionality Improves Learning on Heterophilic Graphs
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T13:34:11.276923Z
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.