Science Visualization and Discursive Knowledge
Loet Leydesdorff

TL;DR
This paper explores the relationship between network data analysis and textual analysis, highlighting their different perspectives and dynamics, and discusses how their integration enhances understanding of complex information systems.
Contribution
It introduces a framework linking social network analysis and latent semantic analysis, emphasizing their coupled dynamics and evolutionary development.
Findings
Different dynamics in network and textual analysis
Historical and system dynamics are interconnected
Evolutionary differentiation enriches information processing
Abstract
Positional and relational perspectives on network data have led to two different research traditions in textual analysis and social network analysis, respectively. Latent Semantic Analysis (LSA) focuses on the latent dimensions in textual data; social network analysis (SNA) on the observable networks. The two coupled topographies of information-processing in the network space and meaning-processing in the vector space operate with different (nonlinear) dynamics. The historical dynamics of information processing in observable networks organizes the system into instantiations; the systems dynamics, however, can be considered as self-organizing in terms of fluxes of communication along the various dimensions that operate with different codes. The development over time adds evolutionary differentiation to the historical integration; a richer structure can process more complexity.
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Taxonomy
TopicsSemantic Web and Ontologies · Biomedical Text Mining and Ontologies · Complex Network Analysis Techniques
