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

2026-03-06

AI Summary

This study addresses the lack of coherence in longitudinal analyses of scientific knowledge graphs, which often stems from inconsistent approaches to topic identification and cross-temporal linkage. To overcome this limitation, the authors propose a unified relational framework that integrates cross-sectional topic detection and longitudinal lineage reconstruction within a single weighted network structure. For the first time, topic evolution is modeled as structural reconfiguration rather than lexical continuity within a relational paradigm. By incorporating relational clustering, directional coverage, centrality-weighted measures of lineage strength, and document membership modeling, the method substantially enhances methodological consistency and interpretive robustness in longitudinal science mapping. This approach enables a more accurate and nuanced understanding of the dynamic mechanisms underlying scientific topic evolution.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Topic discovery and tracking
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

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.

Problem

• thematic evolution
• science mapping
• longitudinal analysis
• structural inconsistency
• relational clustering

Innovation

• thematic evolution
• relational network
• lineage reconstruction
• science mapping
• longitudinal analysis