Computational Mapping of Interdisciplinary Intellectual Linkages in Network Science Using Bibliometric Metadata

Abstract

This paper presents an information technology-driven bibliometric framework for mapping the structural and temporal evolution of interdisciplinary intellectual linkage and apparent conceptual diffusion within the domain of social network analysis. Utilizing large-scale metadata from Web of Science, we construct multiple bibliometric networks representing citation, co-authorship, and keyword relationships. Through computational methods including fractional normalization, Search Path Count (SPC) edge-weighting, and island detection algorithms, we identify latent communities and bibliometric pathways that suggest patterns of intellectual association and citation-mediated influence between social sciences, physics, neuroscience, and behavioral ecology. These computational outputs represent citation-mediated influence patterns and co-occurrence structures, not direct observation of knowledge transmission. Our results highlight the role of digital libraries and algorithmic normalization in addressing terminological and data-quality challenges, alongside name disambiguation procedures and network pruning to mitigate citation boundary issues. This paper contributes to the domain of information technology by demonstrating scalable computational techniques for uncovering hidden intellectual structures and bibliometric evidence of knowledge flows in an increasingly interdisciplinary research landscape.