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

Massimo Aria, Luca D'Aniello, Michelangelo Misuraca, Maria Spano

TL;DR

A structurally integrated framework in which lineage reconstruction is embedded within the same weighted relational architecture that underpins cross-sectional detection and temporal modelling is introduced, thereby conceptualising evolution as the reconfiguration of relational structures rather than simple lexical persistence.

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.

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

TL;DR

A structurally integrated framework in which lineage reconstruction is embedded within the same weighted relational architecture that underpins cross-sectional detection and temporal modelling is introduced, thereby conceptualising evolution as the reconfiguration of relational structures rather than simple lexical persistence.

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.
Paper Structure (20 sections, 15 equations, 10 figures, 1 table)

This paper contains 20 sections, 15 equations, 10 figures, 1 table.

Table of Contents

  1. Introduction
  2. Conceptual and Methodological Background
  3. An Integrated Framework for Longitudinal Thematic Analysis
  4. Cross-Sectional Thematic Representation
  5. Fuzzy Publication-to-Cluster Assignment
  6. Inter-Temporal Assignment and Lineage Strength
  7. Automatic Lineage Detection and Evolutionary Graph
  8. Empirical Application
  9. Cross-Sectional Thematic Structure
  10. Period 1 (2007--2012)
  11. Period 2 (2013--2018)
  12. Period 3 (2019--2025)
  13. Thematic Evolution
  14. Comparative Assessment with Classical Thematic Evolution
  15. Period 1 (2007--2012)
  16. ...and 5 more sections

Figures (10)

  • Figure 1: Annual scientific production of the Journal of Informetrics (2007--2025). Bars are coloured by analytical period; vertical grey lines indicate the two cutting points (end of 2012 and 2018), with period-level document counts shown within each shaded segment.
  • Figure 2: Strategic Diagram: Period 1 (2007--2012).
  • Figure 3: Strategic Diagram: Period 2 (2013--2018).
  • Figure 4: Strategic Diagram: Period 3 (2019--2025).
  • Figure 5: Evolutionary graph.
  • ...and 5 more figures