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Paper Citation Record · LEDGER

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling

As of 20 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2607.25787.

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

pith.paper-citation-record.v1
2607.25787 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T01:32:03.753939Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

50 of 50 outbound references displayed

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  • verified fuzzy0
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External citation measurements

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Outbound references

Observation 924f856a-6d38-485b-a419-28ac3a6cf772 · outbound

This paper cites Saffran, Elizabeth K.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Saffran, Elizabeth K

Reference 1

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Observation 5a555cc1-5730-45a4-9f32-dbc1feb8eaa3 · outbound

This paper cites MIT Press, 2006.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling MIT Press, 2006

Reference 2

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Observation a275991b-7018-4de3-99d8-d167d51eb5f2 · outbound

This paper cites MIT Press, Cambridge, MA, 2007.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling MIT Press, Cambridge, MA, 2007

Reference 3

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Observation bd193d44-201b-4889-ba02-adf65a724634 · outbound

This paper cites Pearce and Geraint A.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Pearce and Geraint A

Reference 4

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Observation 7af171f4-d46f-4d4f-951c-25984baff301 · outbound

This paper cites an unresolved cited work.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Unresolved cited work

Reference 5

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This paper cites an unresolved cited work.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Unresolved cited work

Reference 6

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Observation e84d8535-d792-42c8-bcee-7e76dc584190 · outbound

This paper cites IEEE Transactions on Communications, 32(4):396–402, 1984.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling IEEE Transactions on Communications, 32(4):396–402, 1984

Reference 7

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Observation c782c6c3-77a8-473a-81e4-91a7a166247d · outbound

This paper cites Di Liberto, Claire Pelofi, Roberta Bianco, Prachi Patel, Ashesh D.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Di Liberto, Claire Pelofi, Roberta Bianco, Prachi Patel, Ashesh D

Reference 8

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Observation 09057eb6-2621-412d-a051-2de84792cdd5 · outbound

This paper cites Quiroga-Martinez, Niels C.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Quiroga-Martinez, Niels C

Reference 9

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source=pdf_text observed=2026-08-01T01:31:59.023704Z digest=sha256:66740406b049ea7deca28bab5ab2117ae3dd53a67613e635cc8082915f8111ee

Observation 70d1b554-59cd-4912-9fca-d92782cee405 · outbound

This paper cites Corticalactivityduringnaturalistic music listening reflects short-range predictions based on long-term experience.eLife, 11:e80935,.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Corticalactivityduringnaturalistic music listening reflects short-range predictions based on long-term experience.eLife, 11:e80935,

Reference 10

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Observation ad235caf-5176-45bb-b635-463800fb5213 · outbound

This paper cites Predictive processes and the peculiar case of music.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Predictive processes and the peculiar case of music

Reference 11

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Observation 5b9bfc31-466a-47fb-83b4-afce37aa5ef0 · outbound

This paper cites Zuk, Félix Bigand, Eros Quarta, Stefano Grasso, Flavia Arnese, Andrea Ravignani, Alexandra Battaglia-Mayer, and Giacomo Novembre.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Zuk, Félix Bigand, Eros Quarta, Stefano Grasso, Flavia Arnese, Andrea Ravignani, Alexandra Battaglia-Mayer, and Giacomo Novembre

Reference 12

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correction dated 2025-01-14. Source: crossref record 10.1016/j.cub.2025.01.015->10.1016/j.cub.2023.12.019:correction, observed 2026-07-11T03:17:46.244607+00:00. This notice travels one citation hop only.

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Observation 93f85a2d-3dcd-4834-8403-be2ac712e7e4 · outbound

This paper cites Di Liberto, and Shihab A.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Di Liberto, and Shihab A

Reference 13

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Observation df64d4a6-fe36-436b-af5e-181c4c80845a · outbound

This paper cites Sauvé and Marcus T.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Sauvé and Marcus T

Reference 14

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Observation a141c776-128b-488a-8cb3-97e5f70484c8 · outbound

This paper cites Gold, Marcus T.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Gold, Marcus T

Reference 15

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Observation f4e79097-983b-44cf-a04d-38cf21cb39ba · outbound

This paper cites Predictive processes shape individual musical preferences.Proceedings of the National Academy of Sciences, 122(29):e2500494122, 2025.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Predictive processes shape individual musical preferences.Proceedings of the National Academy of Sciences, 122(29):e2500494122, 2025

Reference 16

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Observation e8e8da0b-fed1-4619-bc89-4752410a2e11 · outbound

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GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Unresolved cited work

Reference 17

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This paper cites an unresolved cited work.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Unresolved cited work

Reference 18

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GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Unresolved cited work

Reference 19

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Observation 82cc65ef-969d-46e4-a954-3e4e70f05e1d · outbound

This paper cites py2lispIDyOM: A Python package for the information dynamics of music (IDyOM) model.Journal of Open Source Software, 7(79):4738, 2022.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling py2lispIDyOM: A Python package for the information dynamics of music (IDyOM) model.Journal of Open Source Software, 7(79):4738, 2022

Reference 20

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Observation fbbf966d-7c99-4df7-87c8-18cf316c96ab · outbound

This paper cites Gold, Giovanni M.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Gold, Giovanni M

Reference 21

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Observation 91ac7af7-63a2-40cf-bc4f-3d53be279829 · outbound

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GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Unresolved cited work

Reference 22

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Observation 997fabe7-55ae-46c4-b990-5c7260416f18 · outbound

This paper cites On the emergence of zipf’s law in music.Physica A: Statistical Mechanics and its Applications, 549:124309, 2020.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling On the emergence of zipf’s law in music.Physica A: Statistical Mechanics and its Applications, 549:124309, 2020

Reference 23

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Observation 8ea74e62-c9cb-400b-8a0d-4004ca7503be · outbound

This paper cites Heaps’ law and vocabulary richness in the history of classical music harmony.EPJ Data Science, 10(1):40, 2021.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Heaps’ law and vocabulary richness in the history of classical music harmony.EPJ Data Science, 10(1):40, 2021

Reference 24

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Observation a96eda47-3768-4d86-b0d1-4915480b7b0d · outbound

This paper cites Stochastic properties of musical time series.Nature Communica- tions, 15:9280, 2024.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Stochastic properties of musical time series.Nature Communica- tions, 15:9280, 2024

Reference 25

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Observation aeec01ad-4f53-4bf5-b86c-9331a356cad0 · outbound

This paper cites The geometry of musical chords.Science, 313(5783):72–74, 2006.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling The geometry of musical chords.Science, 313(5783):72–74, 2006

Reference 26

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Observation 7279a4db-850f-4238-ab6e-6bc9716ec55a · outbound

This paper cites Oxford University Press, New York, 2011.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Oxford University Press, New York, 2011

Reference 27

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Observation d7f6a9d9-e160-4969-968d-e7fef49642d1 · outbound

This paper cites Composingmusicwithcomplexnetworks.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Composingmusicwithcomplexnetworks

Reference 28

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Observation 0ab6e1f9-7651-44c5-b077-6d70548e1f89 · outbound

This paper cites On the complex network structure of musical pieces: analysis of some use cases from different music genres.Multimedia Tools and Applications, 77:16003–16029, 2018.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling On the complex network structure of musical pieces: analysis of some use cases from different music genres.Multimedia Tools and Applications, 77:16003–16029, 2018

Reference 29

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Observation 70603091-d267-4193-a807-118d3e9e4cde · outbound

This paper cites Harmonic structures of Beethoven quartets: a complex network approach.The European Physical Journal B, 95(7):103, 2022.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Harmonic structures of Beethoven quartets: a complex network approach.The European Physical Journal B, 95(7):103, 2022

Reference 30

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Observation b342ebe5-5df9-48d1-a17d-e92a7e3e42f4 · outbound

This paper cites Complexnetworksofharmonicstructureinclassical music.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Complexnetworksofharmonicstructureinclassical music

Reference 31

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Observation 71937b99-e117-449e-881f-fd234327a2cb · outbound

This paper cites David, Christopher W.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling David, Christopher W

Reference 32

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Observation bc82835d-ae8e-49f6-915b-0ba5f41f96e3 · outbound

This paper cites Ángeles Serrano.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Ángeles Serrano

Reference 33

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Observation eaa538ac-9b6f-42a6-be9f-7cd6188a7320 · outbound

This paper cites Tonalharmonyandthetopologyofdynamicalscorenetworks.Journal of Mathematics and Music, 17(2):198–212, 2023.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Tonalharmonyandthetopologyofdynamicalscorenetworks.Journal of Mathematics and Music, 17(2):198–212, 2023

Reference 34

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source=pdf_text observed=2026-08-01T01:32:01.816506Z digest=sha256:3d492c5fe8af4c5fc427875a5ddd70950813e7b49eb0ec76569d792fcb05839e

Observation cf55a7e0-3caa-4a1b-94ea-fdef7f5ef9c1 · outbound

This paper cites Decoding the evolution of melodic and harmonic structure of western music through the lens of network science.Scientific Reports, 16(1):11121, 2026.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Decoding the evolution of melodic and harmonic structure of western music through the lens of network science.Scientific Reports, 16(1):11121, 2026

Reference 35

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Observation 77c2aeb1-91ad-47cd-8794-bd480a874ab8 · outbound

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GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Unresolved cited work

Reference 36

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This paper cites Methods for combining statistical models of music.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Methods for combining statistical models of music

Reference 37

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This paper cites Hagberg, Daniel A.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Hagberg, Daniel A

Reference 38

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This paper cites Gephi: An open source software for exploring and manipulating networks.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Gephi: An open source software for exploring and manipulating networks

Reference 39

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This paper cites Pearce.The Construction and Evaluation of Statistical Models of Melodic Structure in Music Perception and Composition.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Pearce.The Construction and Evaluation of Statistical Models of Melodic Structure in Music Perception and Composition

Reference 40

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Observation f46f89cb-b6d8-4080-99de-20ea34481d6d · outbound

This paper cites IRIDyOM: Exposing musical expectation as an interactive creative space.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling IRIDyOM: Exposing musical expectation as an interactive creative space

Reference 41

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Observation fbb0cf3a-21cf-4292-9bf3-628e5c0d25b9 · outbound

This paper cites Appleton-Century-Crofts,NewYork,1971.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Appleton-Century-Crofts,NewYork,1971

Reference 42

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This paper cites Routledge, 2004.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Routledge, 2004

Reference 43

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Observation 8ab616f7-237a-4a25-8658-a039292b9169 · outbound

This paper cites an unresolved cited work.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Unresolved cited work

Reference 44

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Unavailable: canonical work link unavailable.

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GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Wiggins, and Yukie Nagai

Reference 45

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Observation be8c0145-6a25-4bf0-a5b9-863a4b798ea8 · outbound

This paper cites Computational social creativity.Artificial Life, 21(3):366–378,.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Computational social creativity.Artificial Life, 21(3):366–378,

Reference 46

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Observation f7e990d5-4e5b-475b-a0f5-620c6bfde07e · outbound

This paper cites Extending the creative systems framework for the analysis of creative agent societies.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Extending the creative systems framework for the analysis of creative agent societies

Reference 47

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Observation cc5dbc8f-92a6-4e4a-97f2-a0e2f4312010 · outbound

This paper cites Emergent orchestras: A modular framework for musical robot swarms.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Emergent orchestras: A modular framework for musical robot swarms

Reference 48

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Observation 2d7b4628-1115-4b64-9785-7e12b1060561 · outbound

This paper cites an unresolved cited work.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Unresolved cited work

Reference 2015

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 6f3fdf1c-578d-4b21-8611-63e84f1ccc6f · outbound

This paper cites an unresolved cited work.

GraphIDyOM: A graph-native Python reimplementation of IDyOM for musical expectation modelling Unresolved cited work

Reference 2022

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Pith citing papers

No inbound Pith citation observations are available.