A Reproducible Computational Pipeline for Cross-Database Scientometric Network Construction: Architecture, Algorithms, and Structural Validation

The rapid expansion of scientific publications indexed in multiple bibliographic databases has created new computational challenges for large-scale scientometric analysis. Differences in metadata schemas, identifier structures, and export formats across indexing systems such as Web of Science and Scopus introduce inconsistencies that may distort network-based bibliometric analyses. These issues affect duplicate detection, node identification, and network topology construction. This study proposes a reproducible computational pipeline for cross-database scientometric network construction. The framework formalizes the preprocessing workflow into explicit computational modules, including metadata harmonization, deterministic duplicate detection, sparse graph construction, normalization, and structural diagnostics. The proposed architecture separates preprocessing stages into reproducible algorithmic components, enabling transparent evaluation of methodological assumptions. Empirical evaluation using an interdisciplinary dataset of 317 publications (1990–2023) demonstrate that deterministic preprocessing significantly improves network stability and preserves clustering structure. Structural diagnostics based on modularity, Herfindahl–Hirschman Index, Shannon entropy, and Gini coefficient provide multi-dimensional evaluation of network topology. Scalability experiments confirm near-linear computational growth under sparse graph construction. The principal contribution of this work lies in the formalization of a transparent and extensible computational architecture for reproducible scientometric analysis. The proposed pipeline supports reliable cross-database integration and enables scalable knowledge-mapping applications in interdisciplinary research domains.