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Paper Citation Record · LEDGER

Effective Urban Region Representation Learning Using Heterogeneous Urban Graph Attention Network (HUGAT)

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2202.09021.

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

pith.paper-citation-record.v1
2202.09021 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 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T10:44:27.705584Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T20:03:16.415490Z

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 983abadb-c68d-42d7-bbda-6d7bdc0a9ff4 · inbound

MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations cites this paper.

MobiCLR: Mobility Time Series Contrastive Learning for Urban Region Representations Effective Urban Region Representation Learning Using Heterogeneous Urban Graph Attention Network (HUGAT)

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-09T10:44:27.705584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T10:44:27.705584Z digest=sha256:e3d923680b012f370815c6012746e6b2a1ff6a999fcd2d8ee4bc8534bcbe5725

Observation 59be4ee9-175b-45be-b6fc-c9b4ab2940a4 · inbound

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models cites this paper.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Effective Urban Region Representation Learning Using Heterogeneous Urban Graph Attention Network (HUGAT)

Reference 17

Resolution
verified exact
local_arxiv, observed 2026-08-06T20:03:16.482081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T20:03:12.907871Z digest=sha256:49572b96bc5fa55470f5ddae92abeca8cb45e70c550c76410ff6fb58ff0477c7