A Network Analysis of Retracted Citations by Iranian Computer Scientists

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Zahedi Parsa; Sulaimany Sadegh

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Journal Paper

Paper Information

Journal: International Journal of Web Research Year:2025 | Volume:8 | Issue:4 Page(s): 67-83

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Information Journal Paper

Title

A Network Analysis of Retracted Citations by Iranian Computer Scientists

Author(s)

Zahedi Parsa | Sulaimany Sadegh | Issue Writer Certificate

Keywords

Retraction

Computer science

Iran

Citation analysis

Abstract

Retracted publications continue to influence scholarship long after withdrawal. This study assembles a curated set of 169 Iran-affiliated Retractions in Computer science, data science, and electrical engineering from 2008 to 2024, links them to citing and cited works through two complementary retrieval pipelines, and constructs an expanded citation network of 1'694 nodes and 1,703 edges. We quantify Retraction reasons and timing, community structure, node centrality, self-citation patterns, author and institutional concentration, international co-authorship, and a field-adjusted national benchmark. Misconduct-related causes predominate. The average interval from publication to Retraction increased into 2021 and has since begun to shorten. The citation network exhibits strong community structure with three major thematic clusters. Centrality profiling isolates five retracted works that function as hubs, often reinforced by self-citation loops. Contribution is highly concentrated among a small set of authors and institutions, while collaboration extends across multiple regions beyond Iran. A field-adjusted Retraction rate places the national record among mid-tier producers. These results identify practical leverage points to reduce downstream spread of invalidated findings: persistent indexing flags on hub Retractions, routine screening of citations to retracted work, and focused attention on repeat patterns in self-citation and institutional clusters. The study offers a reproducible dual-pipeline approach, a full centrality profile of an enlarged network, and actor-level diagnostics that support targeted integrity interventions.

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