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arXiv · 2603.06436

Rethinking Thematic Evolution in Science Mapping: An Integrated Framework for Longitudinal Analysis

Massimo Aria·Luca D'Aniello·Michelangelo Misuraca·Maria Spano
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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.

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Massimo Aria, Luca D'Aniello, Michelangelo Misuraca, Maria Spano. 2026-03-06. Rethinking Thematic Evolution in Science Mapping: An Integrated Framework for Longitudinal Analysis. https://arxiv.org/abs/2603.06436

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