Exploring Structural Dynamics in Retracted and Non-Retracted Author's Collaboration Networks: A Quantitative Analysis

Aanchal Sharma; Jazlyn Jose; Kiran Sharma; Raghavraj Sobti; Vansh Saini; Ziya Uddin

REVIEW 4 major objections 5 minor 16 references

Exploring Structural Dynamics in Retracted and Non-Retracted Author's Collaboration Networks: A Quantitative Analysis

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Retracted coauthorship networks are more hierarchical than non-retracted ones, a comparison of 30 authors' collaboration graphs shows.

desk verdict A well-intended descriptive study whose central comparison is undone by a network-size confound and a redundant metric pair. read the letter →

arxiv 2411.17447 v1 pith:PVMQHMEY submitted 2024-11-26 cs.IR

Kiran Sharma , Aanchal Sharma , Jazlyn Jose , Vansh Saini , Raghavraj Sobti , Ziya Uddin This is my paper · ORCID

classification cs.IR

keywords retractionscollaborationnetworksco-authorshipnetworkcentralityscientificmisconductdynamicsstatisticalvalidationego

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper sets out to show that the co-authorship networks formed by retracted publications are structurally different from those formed by the same authors' non-retracted publications. It constructs ego-centered collaboration networks for 30 heavily-retracted researchers, one network from retracted papers and one from non-retracted papers, and compares nine network metrics between the two sets. The central finding is that retracted networks are more hierarchical and centralized, with strong correlations among centrality measures and longer average path lengths, while non-retracted networks are more distributed, with stronger clustering and connectivity. Statistical t-tests and Cohen's d effect sizes are used to argue that the differences in degree centrality, weighted degree, path length, assortativity, eigenvector centrality, and closeness centrality are unlikely to be chance variations. The authors present this as evidence that network topology can flag retraction-prone collaboration patterns, which could inform research-integrity monitoring.

What carries the argument

The carrying mechanism is the paired ego-network comparison: for each of the 30 authors, two collaboration networks are built—one from retracted publications and one from non-retracted publications—and nine standard network metrics are computed for each network: degree centrality, weighted degree, average path length, assortativity, transitivity, clustering coefficient, eigenvector centrality, betweenness centrality, and closeness centrality. The structural difference is then quantified by t-tests on the difference between the retracted and non-retracted metric distributions and by Cohen's d, which measures the effect size in standard-deviation units. The correlation heatmaps of these metrics supply the qualitative picture of hierarchy versus distributed clustering.

What would settle it

Subsample or match the non-retracted papers to the same count and year distribution as the retracted papers for each author, then rerun the t-tests and Cohen's d; if the significant differences in degree centrality, assortativity, and path length disappear or flip, the central claim is not supported.

Watch

Extended reading notes

Core claim

The paper's central claim is that retracted and non-retracted collaboration networks of the same authors are measurably different in structure: retracted networks are organized around a few highly central, hub-like nodes, while non-retracted networks spread influence across many nodes and form more clustered sub-networks. This claim is supported by correlation analyses among metrics and by two-sample t-tests showing statistically significant differences in six of nine metrics, with large Cohen's d values for assortativity, average path length, and eigenvector centrality. The authors interpret the pattern as showing that a hierarchical, centralized collaboration structure is characteristic of retraction-prone research, whereas distributed collaboration with strong clustering is characteristic of healthier publication records.

Load-bearing premise

The load-bearing premise is that the retracted and non-retracted networks of each author are directly comparable, even though the retracted and non-retracted corpora differ greatly in size; if network size drives the metric differences, the claimed structural signature would not be specific to retraction.

Editorial extensions

If this is right

If the claim holds, monitoring metrics such as degree centrality and assortativity in a researcher's collaboration network could serve as an early-warning indicator for retraction-prone patterns.
Hierarchical, hub-dependent networks would be more fragile: the deletion of a few central nodes would disproportionately disrupt a retracted network compared with a non-retracted one.
The metrics that do not differ significantly—transitivity, clustering coefficient, and betweenness centrality—suggest that local cohesion and bridge roles are not what distinguishes retraction-prone collaborations.
Institutional research-integrity policies could use these structural signatures as a screening tool, though the paper notes external factors such as funding and policy are not included.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the size imbalance between the retracted and non-retracted corpora (e.g., 44 vs 679 papers for one author) is not controlled, the observed differences in degree centrality and path length could partly reflect network size rather than retraction status; a matched or size-normalized analysis would settle this.
The same ego-network construction could be applied to authorship records before any retraction occurs, making it possible to test whether high centralization predicts future retractions prospectively rather than retrospectively.
Breaking the analysis down by retraction reason (fabrication, plagiarism, duplication) might reveal that the structural signature is specific to certain kinds of misconduct, a distinction the current aggregate analysis does not make.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The headline finding — retracted networks are more hierarchical and centralized than non-retracted ones — does not survive contact with the data. The comparison is confounded by the much larger size of the non-retracted corpora, and the paper's own tables show that every network has diameter 2, which forces average path length to be a simple transform of degree centrality. The t-tests for those two metrics are therefore two looks at the same signal, not independent evidence.

What is actually new: this is a fresh application of standard ego-network metrics to retracted versus non-retracted publication sets for the same authors, using Retraction Watch and Scopus. I'll credit the data-collection effort; matching 30 authors across sources is real work. The paper also reports Cohen's d and includes all per-author metric tables, which is more transparent than much of the literature.

Where it falls short: (1) The size confound. In Table 1, non-retracted paper counts are often an order of magnitude larger (e.g., Ashok Pandey 44 vs 679). Ego-network metrics like degree centrality, average path length, and closeness centrality all scale with network size, and the authors never control for it. (2) The diameter-2 identity. In a diameter-2 graph, APL = 2 − DC for the normalized degree centrality used here. Every row in Tables 4 and 5 satisfies this, and it is why the t-statistic for APL is exactly the negation of the one for DC. Calling both "significant structural differences" double-counts a single effect. (3) The statistical testing is poorly documented: no summary statistics, no mention of paired vs independent t-tests, and no justification for treating the 30 authors as independent when each contributes both a retracted and a non-retracted network. (4) The sample-selection narrative is internally inconsistent: the text says six authors were excluded and later "10 additional authors" were added to get back to 30, but those 10 names are already in the original Table 1. That undermines confidence in the data handling.

To be fair, the descriptive part — correlations and CCDF plots — is fine as a first pass, and the limitations section acknowledges the small sample. But the central inferential claim is not supported.

Who it's for: readers working on retraction dynamics may want a quick look at the descriptive metrics, but I wouldn't hang a policy or monitoring recommendation on this. It deserves a peer-review round, not a desk reject, because the question is legitimate and the data are real. A referee should send it back for major revision and require the authors to (a) control for network size or match retracted and non-retracted sets by number of publications, (b) drop or reinterpret the redundant APL/DC pair, and (c) clarify the sample selection. If those can't be fixed, the paper should stay an exploratory report, not a comparative claim.

Referee Report

4 major / 5 minor

Summary. This manuscript compares collaboration networks built from retracted and non-retracted publications of 30 authors. The authors report network properties such as degree centrality, average path length, assortativity, and clustering, and use t-tests and Cohen's d to claim that retracted networks are more hierarchical and centralized, while non-retracted networks show distributed collaboration and stronger clustering. The paper concludes that these structural differences could inform policies for research integrity.

Significance. The research question is relevant to the study of scientific misconduct and collaboration dynamics, and a credible finding that retracted co-authorship networks are topologically distinct could inform early-warning systems and research-integrity policy. The manuscript's strengths include its use of public data from Retraction Watch, CrossRef, and Scopus, and the computation of a broad set of network metrics for a relatively large set of authors. However, the central claim is not supported by the evidence as presented: the retracted and non-retracted networks differ systematically in size, several of the reported metrics are mathematically redundant, and the statistical tests ignore the paired structure of the data. No code is provided, and data are only available upon request.

major comments (4)

[§5.4, Tables 2, 4, 5] Every network reported in Tables 4 and 5 has network diameter 2, and the tabulated values exactly satisfy the identity APL = 2 - DC (for example, Ali Nazari retracted: 2 - 0.1496 = 1.8504). For diameter-2 ego-networks, average path length is a deterministic function of average degree centrality, so the t-statistics for Degree Centrality and Average Path Length in Table 2 are exact opposites (-3.2052 and +3.2052). The paper therefore double-counts a single structural signal as two independent significant differences; the APL comparison should be either removed or explicitly acknowledged as redundant.
[§3.1 and Table 1 vs §5.4] The comparison between retracted and non-retracted networks is confounded by corpus and network size: Table 1 shows that for nearly every author the non-retracted publication count is several times larger (e.g., Ashok Pandey 44 vs 679; Fazlul H. Sarkar 53 vs 531). Because the constructed ego-networks all have diameter 2 (the focal author connects all co-authors), the average degree centrality and closeness values are strongly driven by the number of nodes and edges, which in turn are determined by the number of publications. The unpaired t-tests in Section 5.4 do not control for network or corpus size, so the reported significant differences in DC, APL, and closeness may be entirely a size artifact. A size-controlled analysis (matching, covariate adjustment, or permutation with fixed node counts) is essential before the headline claim can be assessed.
[§5.4] The t-tests treat the 30 retracted networks and 30 non-retracted networks as two independent groups, but the two networks associated with a given author are paired observations from the same researcher. An unpaired test ignores this dependence and inflates the effective sample size; a paired t-test or mixed-effects model with author as a random effect is required. The paper should also report the degrees of freedom and explicitly state which test was used.
[§5.3 and §6] The conclusion that retracted networks are 'hierarchical and centralized' is inferred from correlations between degree centrality and eigenvector centrality, but no direct network centralization index (e.g., Freeman's centralization) is computed or tested. The observed correlations may arise from degree distributions or network size rather than from a genuine difference in centralization, and they are not accompanied by significance tests. The authors should measure centralization directly and compare it between groups after controlling for size.

minor comments (5)

[§1] The first paragraph contains a duplicated phrase, 'These findings These findings'; please fix the typo.
[§3.2] The data selection explanation is confusing: six authors are excluded, then a reduced set of 20 is mentioned, then 10 additional authors are added to reach 30; please clarify the final selection process and whether any of the original 30 were replaced.
[§4.1, Eq. (1)] The average path length formula uses ordered pairs, n(n-1); the text should clarify whether the network is treated as directed for this metric and how isolated nodes (if any) are handled.
[Tables 4 and 5] The column header for closeness centrality is 'CoC' in Table 4 and 'CL' in Table 5; please use consistent abbreviations throughout.
[References] The reference list has inconsistencies in author names (e.g., 'Barabasi' vs 'Barabási') and duplicate entries for Barabási (2016); also, some URLs appear incomplete.

Circularity Check

1 steps flagged · score 4.0 of 10

Average path length is exactly 2 minus degree centrality in every analyzed network (all diameters are 2), so the separate t-tests for APL and DC are mirror images; the claimed APL difference is the same signal as the DC difference, not independent evidence.

self definitional [Section 5.3.1–5.3.2; Eq. (1); ND column in Tables 4–5; Table 2]
"On the other hand, a perfect negative correlation between average path length and degree centrality implies that networks with high connectivity (high DC) tend to have shorter average paths, indicating efficient communication. ... However, the same perfect negative correlation between average path length and degree centrality indicates that connectivity remains inversely related to average path length."

Every network in Tables 4 and 5 has ND = 2. For a diameter-2 graph, each ordered pair is at distance 1 (adjacent) or 2 (non-adjacent), so the average path length is L = [2(n-1)-k]/(n-1) = 2 - k/(n-1), exactly 2 minus the normalized degree centrality. The tabulated rows confirm this (e.g., Ali Nazari retracted: 2 - 0.1496 = 1.8504). Consequently, the 'perfect negative correlation' is a mathematical identity, not an empirical discovery about retracted collaboration. The t-statistics in Table 2 are exactly opposite (-3.2052 vs +3.2052), so the significant APL difference is the same statistical signal as the DC difference, double-counted as independent structural evidence.

full rationale

The paper is a descriptive comparison of ego-collaboration networks; it fits no parameters and does not rename a fitted quantity as a prediction. Its self-citations (Sharma 2021, 2024; Sharma & Mukherjee 2024, etc.) motivate the research gap but are not load-bearing for the computed t-tests, which use external data from Retraction Watch and Scopus. However, one construction-level reduction does affect the results section: all reported networks have diameter 2, which forces average path length to equal 2 - degree centrality. The paper notes the 'perfect negative correlation' and reports separate significant t-tests for DC and APL, but those are the same comparison, so the APL finding is not independent confirmation of hierarchical structure. Closeness centrality is also degree-determined in a diameter-2 graph (closeness = 1/[2(n-1)-deg]), further suggesting redundancy, though the paper's primary degree-centrality and assortativity differences are measured facts. The much larger issue of corpus-size confounding (systematically larger non-retracted networks) is a validity threat rather than a circularity; it is not scored here. Overall, the central claim retains some independent content, but one headline-supporting metric reduces by construction.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No free parameters are fitted; the study is empirical. The main unstated assumptions are about the construction of the ego-networks, the distribution of metrics, and the comparability of networks of different sizes.

assumptions (3)

domain assumption The focal author is included as a node in their collaboration network, making all co-authors distance 2 apart.
Tables 4 and 5 report Network Diameter = 2 for all 30 authors in both groups, which is only possible if the author is a central node connected to all co-authors. The methods (Section 4) do not explicitly state this construction.
domain assumption Network metrics are approximately normally distributed, so t-tests are valid.
Section 5.4 states these metrics 'often follow normal or near-normal distributions', which is unlikely for degree centrality and other skewed network measures.
ad hoc to paper Differences in metrics between retracted and non-retracted networks can be attributed to retraction status rather than to differences in corpus size.
The non-retracted publication sets are systematically larger (e.g., Ashok Pandey 44 vs 679, Table 1), yet the paper does not control for network size or paper count in the comparison.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Exploring Structural Dynamics in Retracted and Non-Retracted Author's Collaboration Networks: A Quantitative Analysis." pith.science (2026). https://pith.science/paper/PVMQHMEY

@misc{pith2026241117447,
  author       = {Pith},
  title        = {Pith review of: Exploring Structural Dynamics in Retracted and Non-Retracted Author's Collaboration Networks: A Quantitative Analysis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PVMQHMEY}},
  note         = {Machine review of arXiv:2411.17447}
}

abstract

Retractions undermine the reliability of scientific literature and the foundation of future research. Analyzing collaboration networks in retracted papers can identify risk factors, such as recurring co-authors or institutions. This study compared the network structures of retracted and non-retracted papers, using data from Retraction Watch and Scopus for 30 authors with significant retractions. Collaboration networks were constructed, and network properties analyzed. Retracted networks showed hierarchical and centralized structures, while non-retracted networks exhibited distributed collaboration with stronger clustering and connectivity. Statistical tests, including $t$-tests and Cohen's $d$, revealed significant differences in metrics like Degree Centrality and Weighted Degree, highlighting distinct structural dynamics. These insights into retraction-prone collaborations can guide policies to improve research integrity.

Figures

Figures reproduced from arXiv: 2411.17447 by the authors.

**Figure 1.** Author’s collaboration network of retracted and non-retracted publications. Demonstration of three authors. impacts collaboration efficiency, resilience, and retractions in academic networks. For all 30 authors, the properties of the collaboration network for retracted publications are specified in [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗

**Figure 2.** Correlation plot of network metrics. (left) Retracted only (middle) Non-retracted only, and (right) Retracted vs Non-retracted [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

**Figure 3.** CCDF of author’s collaboration network of retracted and non-retracted publications. Upper: (left) Degree centrality, (middle) Eigenvector centrality, and (right) Closeness centrality. Lower: (left) Weighted degree, (middle) Clustering coefficient, and (right) Betweenness centrality. Where: 𝑠 = √ (𝑛1 − 1)𝑠 2 1 + (𝑛2 − 1)𝑠 2 2 𝑛1 + 𝑛2 − 2 • 𝑋̄ 1 , 𝑋̄ 2 : Mean values of the two groups • 𝑠: Pooled standard deviation • 𝑛… view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

16 extracted references · 11 canonical work pages

[8]

doi:10.1371/journal.pone. 0068397. Yan, E., Ding, Y.,

doi:10.1371/journal.pone
[10]

Furukawa, T., Shirakawa, N., Okuwada, K.,

doi:10.1371/journal.pone.0127556. Furukawa, T., Shirakawa, N., Okuwada, K.,

work page doi:10.1371/journal.pone.0127556
[64]

Cohen, J.,

doi:10.1136/jech-2023-220554. Cohen, J.,

work page doi:10.1136/jech-2023-220554 2023
[1988]

2nd ed., Routledge, New York

Statistical Power Analysis for the Behavioral Sciences. 2nd ed., Routledge, New York. doi:10.4324/9780203771587. Fanelli, D.,

doi:10.4324/9780203771587
[2000]

Proceedings of the National Academy of Sciences of the United States of America 98, 404–409

The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences of the United States of America 98, 404–409. doi:10.1073/pnas.98.2.404. Newman,M.,2004. Coauthorshipnetworksandpatternsofscientificcollaboration. ProceedingsoftheNationalAcademyofSciencesoftheUnited States of America 101, 5200 –

doi:10.1073/pnas.98.2.404 2004
[2001]

Physica A-statistical Mechanics and Its Applications 311, 590–614

Evolution of the social network of scientific collaborations. Physica A-statistical Mechanics and Its Applications 311, 590–614. doi:10.1016/S0378-4371(02)00736-7. Barabási, A.L.,

work page doi:10.1016/s0378-4371(02)00736-7
[2009]

Journal of the Association for Information Science and Technology doi:10.1002/ASI.V60:10

Applying centrality measures to impact analysis: A coauthorship network analysis. Journal of the Association for Information Science and Technology doi:10.1002/ASI.V60:10. Page 14 of 13

work page doi:10.1002/asi.v60:10
[2011]

Scientometrics 87, 451–466

Quantitative analysis of collaborative and mobility networks. Scientometrics 87, 451–466. doi:10.1007/s11192-011-0360-7. Gross, C.,

work page doi:10.1007/s11192-011-0360-7

Show all 16 references

[2013]

PLoS Medicine 10, e1001563

Why growing retractions are (mostly) a good sign. PLoS Medicine 10, e1001563. URL:https://journals.plos.org/ plosmedicine/article?id=10.1371/journal.pmed.1001563, doi:10.1371/journal.pmed.1001563. Page 12 of 13 Table 5 Network properties of non-retracted author’s collaboration...

doi
[2015]

Sociology of Innovation eJournal doi:10.1016/J.RESPOL.2017.07.003

The career effects of scandal: Evidence from scientific retractions. Sociology of Innovation eJournal doi:10.1016/J.RESPOL.2017.07.003. Barabasi, A.L.,

2017 doi
[2016]

Annual review of psychology 67, 693–711

Scientific misconduct. Annual review of psychology 67, 693–711. doi:10.1146/annurev-psych-122414-033437. Hussinger, K., Pellens, M.,

doi
[2018]

AARN: Other Anthropology of Education (Topic) doi:10.2139/ssrn.3072290

Guilt by association: How scientific misconduct harms prior collaborators. AARN: Other Anthropology of Education (Topic) doi:10.2139/ssrn.3072290. Jin,G.,Jones,B.F.,Lu,S.F.,Uzzi,B.,2019. Thereversemattheweffect: Consequencesofretractioninscientificteams. ReviewofEconomicsand S...

2019 doi
[2020]

Sharma,K.,2024

Scientometrics 126, 8363–8374. Sharma,K.,2024. Overtwodecadesofscientificmisconductinindia: Retractionreasonsandjournalqualityamonginter-countryandintra-country institutional collaboration. Scientometrics , 1–23. Sharma, K., Khurana, P.,

2024
[2021]

Team size and retracted citations reveal the patterns of retractions from 1981 to

1981
[2023]

Journal of ResearchIntegrity12,145–160

The reputational impact of retracted publications: Evidence from academic citations and funding. Journal of ResearchIntegrity12,145–160. URL: https://www.sciencedirect.com/science/article/pii/S0039292023000128,doi: 10.1016/ j.resinn.2023.101028. Newman, M.,
[5205]

Newman, M.E.,

doi:10.1073/PNAS.0307545100. Newman, M.E.,

doi

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [8]

doi:10.1371/journal.pone. 0068397. Yan, E., Ding, Y.,

doi:10.1371/journal.pone

[2] [10]

Furukawa, T., Shirakawa, N., Okuwada, K.,

doi:10.1371/journal.pone.0127556. Furukawa, T., Shirakawa, N., Okuwada, K.,

work page doi:10.1371/journal.pone.0127556

[3] [64]

Cohen, J.,

doi:10.1136/jech-2023-220554. Cohen, J.,

work page doi:10.1136/jech-2023-220554 2023

[4] [1988]

2nd ed., Routledge, New York

Statistical Power Analysis for the Behavioral Sciences. 2nd ed., Routledge, New York. doi:10.4324/9780203771587. Fanelli, D.,

doi:10.4324/9780203771587

[5] [2000]

Proceedings of the National Academy of Sciences of the United States of America 98, 404–409

The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences of the United States of America 98, 404–409. doi:10.1073/pnas.98.2.404. Newman,M.,2004. Coauthorshipnetworksandpatternsofscientificcollaboration. ProceedingsoftheNationalAcademyofSciencesoftheUnited States of America 101, 5200 –

doi:10.1073/pnas.98.2.404 2004

[6] [2001]

Physica A-statistical Mechanics and Its Applications 311, 590–614

Evolution of the social network of scientific collaborations. Physica A-statistical Mechanics and Its Applications 311, 590–614. doi:10.1016/S0378-4371(02)00736-7. Barabási, A.L.,

work page doi:10.1016/s0378-4371(02)00736-7

[7] [2009]

Journal of the Association for Information Science and Technology doi:10.1002/ASI.V60:10

Applying centrality measures to impact analysis: A coauthorship network analysis. Journal of the Association for Information Science and Technology doi:10.1002/ASI.V60:10. Page 14 of 13

work page doi:10.1002/asi.v60:10

[8] [2011]

Scientometrics 87, 451–466

Quantitative analysis of collaborative and mobility networks. Scientometrics 87, 451–466. doi:10.1007/s11192-011-0360-7. Gross, C.,

work page doi:10.1007/s11192-011-0360-7