Rethinking Thematic Evolution in Science Mapping: An Integrated Framework for Longitudinal Analysis
Abstract
Strategic diagrams and co-word analysis are widely employed to examine the conceptual structure of scientific domains and their development over time. Yet a structural inconsistency characterises dominant longitudinal implementations: themes are detected through relational clustering in weighted networks, whereas their inter-temporal connections are commonly inferred from set-theoretic overlap among keywords or core documents. This study introduces a structurally integrated framework in which lineage reconstruction is embedded within the same weighted relational architecture that underpins cross-sectional detection. The approach models thematic continuity through graded document affiliation and a lineage-strength measure that combines directional coverage with centrality-weighted structural relevance, thereby conceptualising evolution as the reconfiguration of relational structures rather than simple lexical persistence. By aligning thematic detection and temporal modelling within a unified relational paradigm, the framework enhances the methodological coherence and interpretive robustness of longitudinal science mapping.
Cite
@article{arxiv.2603.06436,
title = {Rethinking Thematic Evolution in Science Mapping: An Integrated Framework for Longitudinal Analysis},
author = {Massimo Aria and Luca D'Aniello and Michelangelo Misuraca and Maria Spano},
journal= {arXiv preprint arXiv:2603.06436},
year = {2026}
}