Rethinking Thematic Evolution in Science Mapping: An Integrated Framework for Longitudinal Analysis
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910043814756352 |
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| author | Aria, Massimo D'Aniello, Luca Misuraca, Michelangelo Spano, Maria |
| author_facet | Aria, Massimo D'Aniello, Luca Misuraca, Michelangelo Spano, Maria |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_06436 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Rethinking Thematic Evolution in Science Mapping: An Integrated Framework for Longitudinal Analysis Aria, Massimo D'Aniello, Luca Misuraca, Michelangelo Spano, Maria Social and Information Networks Digital Libraries 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. |
| title | Rethinking Thematic Evolution in Science Mapping: An Integrated Framework for Longitudinal Analysis |
| topic | Social and Information Networks Digital Libraries |
| url | https://arxiv.org/abs/2603.06436 |