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Paper Citation Record · LEDGER

Applying Hybrid Graph Neural Networks to Strengthen Credit Risk Analysis

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2410.04283.

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

pith.paper-citation-record.v1
2410.04283 v1

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 2026-08-13T06:32:02.005865+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:55:20.874631Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-12T11:03:15.322618Z

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 6ac17f44-058f-4e87-816e-8ef0528f6597 · inbound

Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification cites this paper.

Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification Applying Hybrid Graph Neural Networks to Strengthen Credit Risk Analysis

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-12T17:53:45.897816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:53:45.897816Z digest=sha256:a58c5fee0dee7a28b2945de4effc3385bf91f4bfd8ab732266e5fccdb56a5489

Observation 8be1b810-02f0-447c-8a8f-f46c106f239a · inbound

A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation cites this paper.

A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation Applying Hybrid Graph Neural Networks to Strengthen Credit Risk Analysis

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-12T17:55:20.874631Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:55:20.874631Z digest=sha256:f6cd0f590321977fa946e18c5eed517bed99569eb56bf66d9f3e6ebe173f679c

Observation f9fc159e-b141-4942-9dec-7fac1c13913e · inbound

Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches cites this paper.

Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches Applying Hybrid Graph Neural Networks to Strengthen Credit Risk Analysis

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-12T13:01:12.862430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:01:12.862430Z digest=sha256:1afaee6be99a33370c8885cc1c9c73fc7f773bd4c110efe19e49a418cd10f3bc

Observation b1494931-853b-423f-a1aa-1906f1b3ee0d · inbound

Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Data cites this paper.

Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Data Applying Hybrid Graph Neural Networks to Strengthen Credit Risk Analysis

Reference 11

Resolution
verified exact
local_arxiv, observed 2026-08-12T11:03:15.328165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T11:03:15.140474Z digest=sha256:0493489133432c87c6129fcd12bb085a79cfd1c478c0416d63035f435e4564f3