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

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

TL;DR

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 to gain insights into retraction-prone collaborations.

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.

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

TL;DR

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 to gain insights into retraction-prone collaborations.

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 -tests and Cohen's , 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.

Paper Structure

This paper contains 29 sections, 12 equations, 3 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Literature review
  3. Research gap
  4. Research objectives
  5. Data description
  6. Data collection
  7. Data pre-processing
  8. Methodology
  9. Path length
  10. Weighted edges
  11. Clustering coefficient
  12. Assortativity
  13. Transitivity
  14. Centrality measures
  15. Degree centrality
  16. ...and 14 more sections

Figures (3)

  • Figure 1: Author's collaboration network of retracted and non-retracted publications. Demonstration of three authors.
  • Figure 2: Correlation plot of network metrics. (left) Retracted only (middle) Non-retracted only, and (right) Retracted vs Non-retracted.
  • 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.