DOI: 10.6071/m3g674
title: The disruption index suffers from citation inflation and is confounded by shifts in scholarly citation practice: synthetic citation networks for bibliometric null models

description: We demonstrate that the disruption index (CD) recently applied to
 publication and patent citation networks by Park et al. (Nature, 2023)
 systematically decreases over time due to secular growth in research and
 patent production, following two distinct mechanisms unrelated to
 innovation – the first structural and the second behavioral. The
 structural explanation follows from ‘citation inflation’ (CI) (Petersen et
 al., Research Policy, 2018), an inextricable feature of real citation
 networks. One driver of CI is the ever-increasing length of reference
 lists, which causes the CD index to systematically decrease. The
 behavioral explanation reflects shifts in scholarly citation practice
 (e.g. self-citation) that increase the rate of triadic closure in citation
 networks and confounds efforts to measure disruptive innovation using CD.
 Combined, these two mechanisms render CD unsuitable for cross-temporal
 analysis, and call into question the interpretations provided by Park et
 al.
description: Enclosed data accompany the following publications:
 Alexander M. Petersen, Felber Arroyave, Fabio
 Pammolli (2025). <em>The disruption index suffers from citation
 inflation: re-analysis of temporal CD trend and relationship with team
 size reveal discrepancies. J. Informetrics 19, 101605 (2025).
 DOI:10.1016/j.joi.2024.101605</em> Alexander M. Petersen, Felber Arroyave, Fabio Pammolli (2024). <em>The disruption index is biased by citation inflation. Quantitative Science Studies 5, 936-953 (2024). DOI:10.1162/qss_a_00333</em> To summarize, enclosed are two types of data: 1) Empirical publication-level data accompanied by code (do-files) for running multi-variate regressions in STATA 2) Raw network data produced for 6 citation network scenarios. For each scenario, we include 4 synthetic networks each, for a total of 24 citation networks. Each citation network is comprised of 125270 nodes that were systematically added in cohorts, therefore representing a null model for evolving citation networks, and thereby useful for benchmarking existing and new bibliometric measures. These data were generated using a synthetic citation network model developed and reported in: Pan, R. K., Petersen, A. M., Pammolli, F. & Fortunato, S. <em>The memory of science: Inflation, myopia, and the knowledge network.</em> Journal of Informetrics 12, 656–678 (2018).  
description: Enclosed code was developed using 1) STATA 13.0 and 2)
 Mathematica 13 software, both of which should be backwards compatible with
 newer software verions. The document README.pdf provides detailed
 descriptions of the enclosed data and code.
description: # The disruption index suffers from citation inflation and is confounded
 by shifts in scholarly citation practice: synthetic citation networks for
 bibliometric null models
 [https://doi.org/10.6071/M3G674](https://doi.org/10.6071/M3G674) ##
 Description of the data and file structure Enclosed are two types of data:
 1) Empirical publication-level data (tabular file format) accompanied by
 code (do-files) for running multi-variate regressions in STATA; 2)  Raw
 network data (sparse network representation format) produced for 6
 citation network scenarios. For each scenario we include 4 synthetic
 networks each, for a total of 24 citation networks. Each citation network
 is comprised of 125270 nodes that were systematically added in cohorts,
 therefore representing null model for evolving citation networks, and
 thereby useful for benchmarking existing and new bibliometric measures.
 The data and code for 1) and 2) are organized into subfolders, the
 contents and functionality of which are described in detail in the
 enclosed README.pdf document. ### Files and variables #### File:
 README.pdf **Description:** An extended description of the files and their
 data format #### File: Dryad\_OpenData.zip **Description:** A folder
 containing the synthetic networks and Mathematica notebooks (code) for
 visualizing the results ## Code/software Data were analyzed and visualized
 using notebooks developed with Mathematica 13.0, which should be
 compatible with future software versions. The workflow for executing
 Mathematica notebooks is simply Shift+Enter to execute commands contained
 in any given cell; the initial cells upload the data files, and from there
 the notebook cells should be executed from start to end in linear order.
 Multivariate regression models were implemented with STATA 13.0 software,
 which should be compatible with future software versions. The workflow for
 executing STATA do-files is to open the .dta file containing the tabular
 observation data, and then to execute the associated .do file containing
 the model specification. ### Version changes **Feb-2025:** Added the
 folders /QSS_STATA_Table1 and /JOI_STATA_Table_S3&S4, which contain
 tabular data and code that generate several multi-variate regressions
 using STATA 13 software; these models test relationships between the
 disruption index and various other publication-level covariates (team
 size, citations, and reference-list length) for millions of publications,
 as reported in the published companion articles.