Radek Martinek | Searcharxiv

arXiv2026

Unveiling the Predators: Contemporary Approaches to Identifying Illegitimate Open Access Journals in the Academic Publishing Ecosystem

Predatory journals pose a significant challenge to the integrity of the Open Access (OA) publishing model by exploiting its framework for financial gain while bypassing essential editorial and peer-review standards. This study critically evaluates existing methodologies for identifying such journals, ranging from manual blacklist checks to advanced automated approaches utilizing machine learning. The analysis highlights critical limitations, including the lack of a universally accepted definition of predatory journals, over-reliance on binary classification systems (e.g., blacklists and whitelists), and issues with scalability, reliability and interpretability. To address these shortcomings, this paper introduces a novel methodology based on multivariate graph analysis. By modeling the academic publishing ecosystem as a network of interconnected entities (such as authors, articles, journals, and publishers), this approach provides broader insights into the dynamics of scholarly communication and could help identify illegitimate publishing practices by utilizing graph algorithms like centrality measures, community detection, and anomaly detection. The proposed framework aims to enhance the accuracy, scalability, and transparency of detection of illegitimate journals and publishing practices while fostering a more comprehensive understanding of the academic publishing landscape.

cs.DL↗

arXiv2026

A Graph Approach to the Academic Publishing Network: A Heterogeneous Model and Structural Screening over OpenAlex Open Data

The academic publishing ecosystem is a vast, heterogeneous network of works, authors, institutions, journals, and topics. Traditional scientometrics reduces it to isolated tabular indicators (h-index, Impact Factor) that ignore topological context and are not designed to capture coordinated illegitimate practices. Building on our companion review, which proposed graph analysis of publishing integrity, this paper implements that approach. We define a heterogeneous multivariate graph model over OpenAlex open data (seven node types, seven edge types) and a methodology based on projections (citation and co-authorship networks), interpretable structural metrics, community detection, and three screening detectors of anomalous publishing patterns. We deliberately avoid binary classification: detectors return ranked candidates with explicit structural evidence for human assessment. On the institutional corpus of VSB - Technical University of Ostrava (2020-2025) with its one-hop citation neighbourhood, community detection recovers real research groups, centralities identify cross-disciplinary bridges, and the screenings flag dense co-authorship cliques, locally closed citation loops, and thematically isolated venues. On a second, venue-centric corpus with external ground truth (journals delisted by Scopus and DOAJ) and size-matched controls, a naive case-control design yields seemingly strong but spurious detectors (a prominence confound), whereas after matching the only robust signal is the breadth of disciplinary scope (AUC 0.70); an open graph-based prestige measure (PageRank over the journal citation network) tracks a JIF proxy while being an order of magnitude more resistant to citation gaming than count-based indicators. We release the method as the open-source library apnet with a reproducible CLI workflow and a web interface; the analysis runs on commodity hardware in minutes.

cs.SI↗