Paper Citation Record · LEDGER

Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach

As of 13 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2510.20454.

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

pith.paper-citation-record.v1
2510.20454 v2

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T08:31:23.195077Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

verified exact14
verified fuzzy0
unresolved14
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External citation measurements

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Outbound references

Observation 1877afc7-8d27-4fd4-9623-bdca63ea3fbf · outbound

This paper cites an unresolved cited work.

Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach Unresolved cited work

Reference 1

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This paper cites European Journal of Operational Research 248, 211–218.

Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach European Journal of Operational Research 248, 211–218

Reference 7

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This paper cites International Journal of Photoenergy 2022, 1898132.

Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach International Journal of Photoenergy 2022, 1898132

Reference 12

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This paper cites Information Systems and e-Business Management 20, 551–580.

Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach Information Systems and e-Business Management 20, 551–580

Reference 13

Resolution: verified exact; doi, observed 2026-08-04T08:33:27.199157Z

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This paper cites Journal of Quantitative Analysis in Sports 12, 127–138.

Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach Journal of Quantitative Analysis in Sports 12, 127–138

Reference 18

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This paper cites Master’s thesis.

Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach Master’s thesis

Reference 21

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This paper cites an unresolved cited work.

Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach Unresolved cited work

Reference 23

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Observation 90c7868b-6d7e-46ce-8e29-14d642299b97 · outbound

This paper cites Empirical Economics 69(4).

Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach Empirical Economics 69(4)

Reference 25

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Observation 322e85bf-5c76-46e3-9f6d-5bf1a26577ab · outbound

This paper cites Central European Journal of Operations Research 27, 533–549.

Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach Central European Journal of Operations Research 27, 533–549

Reference 27

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Observation a813c0cf-d040-4ccd-acae-b5d33b61176c · outbound

This paper cites doi:10.1016/j.ijforecast.2019.08.009.

Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach doi:10.1016/j.ijforecast.2019.08.009

Reference 28

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Observation 4ae491ec-9b3d-4e62-ba24-564efa650f9f · outbound

This paper cites European Journal of Sport Science 21(7), 944–957.

Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach European Journal of Sport Science 21(7), 944–957

Reference 29

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Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach Unresolved cited work

Reference 30

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Observation 7bf42b10-1fa5-4b57-8ff0-e32b09e2a659 · outbound

This paper cites 27003–27015.

Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach 27003–27015

Reference 31

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Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach Unresolved cited work

Reference 33

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Observation 5f812e56-93c3-4102-ab49-0a0a3160a735 · outbound

This paper cites The dataset differs from the one we use here in that it only considers men’s matches and excludes any 500-point matches other than the Halle Open and the Queen’s Club Championships.

Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach The dataset differs from the one we use here in that it only considers men’s matches and excludes any 500-point matches other than the Halle Open and the Queen’s Club Championships

Reference 34

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This paper cites This extension provides further justification for the proper hyperparameter tuning and improved graph structure in this work.

Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach This extension provides further justification for the proper hyperparameter tuning and improved graph structure in this work

Reference 35

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Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach Monthly weather review 78, 1–3

Reference 1950

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Observation 7f9d83eb-e39f-497a-9bdb-02bdd39bd151 · outbound

This paper cites The Bell Sys- tem Technical Journal 35(4), 917–926.

Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach The Bell Sys- tem Technical Journal 35(4), 917–926

Reference 1956

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Observation 8d68d89a-07e7-4d03-9378-cd7269e45295 · outbound

This paper cites The Economic Journal 103(420), 1141–1153.

Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach The Economic Journal 103(420), 1141–1153

Reference 1993

Resolution: verified exact; doi, observed 2026-08-04T08:33:26.354077Z

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Observation 126488e4-8fa6-4de3-9644-98e768595cde · outbound

This paper cites JournaloftheRoyalStatistical Society: Series C (Applied Statistics) 46, 265–280.

Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach JournaloftheRoyalStatistical Society: Series C (Applied Statistics) 46, 265–280

Reference 1997

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Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach International Transactions in Operational Research 7, 585–594

Reference 2000

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Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach Journal of Quantitative Analysis in Sports 4(2)

Reference 2008

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Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach doi:10.17713/ajs.v38i4

Reference 2009

Resolution: verified exact; doi, observed 2026-08-04T08:33:26.987863Z

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Observation 06e4e8f6-ab10-4212-a4e1-fe3593d4ba43 · outbound

This paper cites International Journal of Forecasting 27(2), 619–630.

Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach International Journal of Forecasting 27(2), 619–630

Reference 2011

Resolution: verified exact; doi, observed 2026-08-04T08:33:26.829015Z

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This paper cites (Eds.), Com- puter Performance Engineering, Springer, Berlin, Heidelberg.

Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach (Eds.), Com- puter Performance Engineering, Springer, Berlin, Heidelberg

Reference 2013

Resolution: verified exact; doi, observed 2026-08-04T08:33:27.346224Z

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Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach FiveThirtyEight

Reference 2015

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Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach (Eds.), Complex Networks VII

Reference 2016

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Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach International Journal of Forecasting 33(2), 458–466

Reference 2017

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Observation fd460671-51bd-4f00-a37e-8bb18162baf2 · outbound

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Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach 2623–2631

Reference 2019

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Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach International Journal of Forecasting 36(4), 1329–1341

Reference 2020

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Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach (Eds.), SOFSEM 2021: Theory and Practice of Computer Science, Springer International Publishing, Cham

Reference 2021

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Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach European Journal of Operational Research 297, 120–132

Reference 2022

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Observation a103b8a6-0945-487b-81d4-97d91aa8ee18 · outbound

This paper cites Annals of Operations Research 325, 615–632.

Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach Annals of Operations Research 325, 615–632

Reference 2023

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This paper cites Elo Ratings in the Presence of Intransitivity.

Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach Elo Ratings in the Presence of Intransitivity

Reference 2024

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This paper cites International Journal of Forecasting 41(2), 803–820.

Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach International Journal of Forecasting 41(2), 803–820

Reference 2025

Resolution: verified exact; doi, observed 2026-08-04T08:33:27.087997Z

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Pith citing papers

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