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Paper Citation Record · LEDGER

The CTU Prague Relational Learning Repository

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:1511.03086.

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

pith.paper-citation-record.v1
1511.03086 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 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 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:05:07.741940Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T06:09:37.809889Z

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 13928cd1-3925-425b-b5f7-1772c6d84f0a · inbound

A Layered Aggregate Engine for Analytics Workloads cites this paper.

A Layered Aggregate Engine for Analytics Workloads The CTU Prague Relational Learning Repository

Reference 42

Resolution
verified exact
arxiv_id, observed 2026-05-25T18:56:09.171231Z

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=2026-05-25T18:51:26.166895Z digest=sha256:9e11eba3e5935da70c933291c19c79d400ca6eba2b60317bc401845dc0c155bf

Observation feb47191-f060-4af5-aa9c-1c8e2111d24e · inbound

Joint Relational Database Generation via Graph-Conditional Diffusion Models cites this paper.

Joint Relational Database Generation via Graph-Conditional Diffusion Models The CTU Prague Relational Learning Repository

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-05-22T13:14:53.354462Z

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=2026-05-22T13:12:30.940265Z digest=sha256:145214a2c96bfb4afa28379f9c9d04176dfb5bd86a904a52e9217daafafbf4ac

Observation d28ebc76-d852-4498-99b0-97316a9921f6 · inbound

RelDiff: Relational Data Generative Modeling with Graph-Based Diffusion Models cites this paper.

RelDiff: Relational Data Generative Modeling with Graph-Based Diffusion Models The CTU Prague Relational Learning Repository

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-07T12:05:07.741940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:05:07.741940Z digest=sha256:a0c65777246bd05f77dc2ff4d0ad644245ff1e3126a8b71d36cfd62c9c711069

Observation 82a257dd-9933-4406-bfe5-7a95ce8a0e3a · inbound

From Features to Structure: Task-Aware Graph Construction for Relational and Tabular Learning with GNNs cites this paper.

From Features to Structure: Task-Aware Graph Construction for Relational and Tabular Learning with GNNs The CTU Prague Relational Learning Repository

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-07T11:31:43.118565Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:31:43.118565Z digest=sha256:6f094a5a442b48adcb1a59ed9471f17b8e35bea782241845a33be8e16d323bd6

Observation b6c3adc1-cd07-4638-aea4-a3a742021262 · inbound

Rel-HNN: Split Parallel Hypergraph Neural Network for Learning on Relational Databases cites this paper.

Rel-HNN: Split Parallel Hypergraph Neural Network for Learning on Relational Databases The CTU Prague Relational Learning Repository

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-06T16:49:10.981563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:49:10.981563Z digest=sha256:9155b2eb605501379a7a79ebb48806bf40843ffd30595b237322cee1b4a57f71

Observation d5cbbc5b-13d6-4132-9542-3e174d9ab221 · inbound

Synthesize, Retrieve, and Propagate: A Unified Predictive Modeling Framework for Relational Databases cites this paper.

Synthesize, Retrieve, and Propagate: A Unified Predictive Modeling Framework for Relational Databases The CTU Prague Relational Learning Repository

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-05T22:17:47.574456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:17:47.574456Z digest=sha256:7aec1a16f9e2f730bb51ba1f56149dfbbfef5e39761a6a721d538c0252d92873

Observation 6e1926bf-02b9-445e-a8f9-3e13bf2e3c24 · inbound

RelBench v2: A Large-Scale Benchmark and Repository for Relational Data cites this paper.

RelBench v2: A Large-Scale Benchmark and Repository for Relational Data The CTU Prague Relational Learning Repository

Reference 9

Resolution
metadata mismatch
arxiv_id, observed 2026-05-16T02:02:06.863931Z

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=2026-05-16T02:01:21.704891Z digest=sha256:3ab69615c538f61b9b17ee62a1243b1dad09563108a999d256b210bba98dc251

Observation 2ebc4a0e-6963-4451-b7b9-d13b740932a1 · inbound

No Need to Train Your RDB Foundation Model cites this paper.

No Need to Train Your RDB Foundation Model The CTU Prague Relational Learning Repository

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-02T23:33:32.061746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T23:33:32.061746Z digest=sha256:557b4b848dabece18f3442de0a814b9415897932b9a6c7642b677f90df91d45c

Observation 42f668ef-3809-4396-9f4e-9e8f166c0f5e · inbound

Universal Encoders for Modular Relational Deep Learning cites this paper.

Universal Encoders for Modular Relational Deep Learning The CTU Prague Relational Learning Repository

Reference 19

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T06:09:37.811762Z

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=2026-06-26T14:44:38.182752Z digest=sha256:c59c1b6f65efc956eff4a1036d41ebfd6521194081608dd62fc695aadff696b5

Observation 48128bf9-00dd-41f0-8f04-892a7ff65240 · inbound

Parameter-Free Encoders Remain Viable for RDB Foundation Models cites this paper.

Parameter-Free Encoders Remain Viable for RDB Foundation Models The CTU Prague Relational Learning Repository

Reference 14

Resolution
unresolved
no resolver link, observed 2026-07-11T11:47:14.742492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-11T11:47:14.742492Z digest=sha256:0af07a388944af69eb83fda24059a40e9600ab3990885b9923fd0ae58c60ccc7

Observation 52569540-c1a8-41dc-991a-8d411e141d90 · inbound

Parameter-Free Encoders Remain Viable for RDB Foundation Models cites this paper.

Parameter-Free Encoders Remain Viable for RDB Foundation Models The CTU Prague Relational Learning Repository

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-02T08:36:03.665812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T08:36:03.665812Z digest=sha256:bb0f305d36baae7bcc2746e66b9411bbcdf47bfd9c816b792aca808974020a1e