[Sigmetrics] Structural Variation Analysis (SVA)

[Chen,Chaomei]

The Structural Variation Analysis (SVA) is a computational analytic procedure that aims to identify the novelty of a scientific publication based on how much it differs from its peers' citation patterns.

The underlying theory and a few concept-proving case studies appeared in a 2012 paper in JASIST. The function is now available in the latest release of CiteSpace (4.0 Release 1).

CiteSpace is a freely available Java application for visualizing trends and patterns in scientific literature.

Feedback, comments, and suggestions are welcome.

Download:
https://sites.google.com/site/citespace101/download

Theory:
https://sites.google.com/site/citespace101/0-2-show-cases/2-7-theory-of-structural-variation

Procedure:
https://sites.google.com/site/citespace101/11-advanced-topics/11-3structural-variation-analysis-sva

References:

  *   Chen, C. (2012) Predictive effects of structural variation on citation counts<http://dx.doi.org/10.1002/asi.21694>. Journal of the American Society for Information Science and Technology, 63(3), 431-449.
  *   Chen, C. (2014) The Fitness of Information: Quantitative Assessments of Critical Evidence<http://www.amazon.com/gp/product/1118128338/ref=as_li_tl?ie=UTF8&camp=1789&creative=9325&creativeASIN=1118128338&linkCode=as2&tag=chaomeichensh-20&linkId=6HTSBJWVXIDL43E3>. Wiley.

Best wishes,
Chaomei Chen
Drexel University
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