Main-path analysis and path-dependent transitions in HistCite(TM)-based historiograms
Diana Lucio-Arias, Loet Leydesdorff
TL;DR
This paper enhances HistCite(TM) by integrating social network analysis and information theory algorithms to improve the visualization and understanding of scientific development trajectories.
Contribution
It introduces novel algorithms to extend HistCite(TM)'s capabilities for analyzing and visualizing scientific historiograms with richer network insights.
Findings
Improved visualization of citation networks
Identification of main paths in scientific development
Enhanced understanding of path-dependent transitions
Abstract
With the program HistCite(TM) it is possible to generate and visualize the most relevant papers in a set of documents retrieved from the Science Citation Index. Historical reconstructions of scientific developments can be represented chronologically as developments in networks of citation relations extracted from scientific literature. This study aims to go beyond the historical reconstruction of scientific knowledge, enriching the output of HistCite(TM) with algorithms from social network analysis and information theory.
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Taxonomy
TopicsSemantic Web and Ontologies · Data Visualization and Analytics