The structure and dynamics of cocitation clusters: A multiple-perspective cocitation analysis

By Chaomei Chen, Fidelia Ibekwe-Sanjuan, Jia-lin Hou. J. Assoc. Inf. Sci. Technol., 2010

1,592 citations recorded by the source corpus.

A multiple-perspective co-citation analysis method is introduced for characterizing and interpreting the structure and dynamics of co-citation clusters. The method facilitates analytic and sense making tasks by integrating network visualization, spectral clustering, automatic cluster labeling, and text summarization. Co-citation networks are decomposed into co-citation clusters. The interpretation of these clusters is augmented by automatic cluster labeling and summarization. The method focuses on the interrelations between a co-citation cluster's members and their citers. The generic method is applied to a three-part analysis of the field of Information Science as defined by 12 journals published between 1996 and 2008: 1) a comparative author co-citation analysis (ACA), 2) a progressive ACA of a time series of co-citation networks, and 3) a progressive document co-citation analysis (DCA). Results show that the multiple- perspective method increases the interpretability and accountability of both ACA and DCA networks.

Subfields: Computer Vision and Pattern Recognition, Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/da8f4923e3be1d238809f0f1d3a6a23a02fd6994

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Citations in the corpus

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Emerging trends in regenerative medicine: a scientometric analysis in CiteSpace (2012, methodology): “It is worth noting that this expert opinion is solely based on scientometric patterns revealed by CiteSpace without prior working experience in the regenerative medicine field.”
Visualizing a field of research: A methodology of systematic scientometric reviews (2019, background): “The silhouette score of a network measures the average homogeneity of derived clusters (45).”
SciMAT: A new science mapping analysis software tool (2012, methodology): “Dimensionality reduction techniques such as principal component analysis or multidimensional scaling, clustering algorithms (Chen et al., 2010; Chen & Redner, 2010; Coulter et al., 1998; Kandylas, Upham, & Ungar, 2010; Rosvall & Bergstrom, 2010; Small and Sweeney, 1985), and Pathfinder networks…”
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