Summary Clustering algorithms are used prominently in co-citation analysis by analysts aiming to reveal research streams within a field. However, clustering of widely cited articles is not robust to small variations in citation patterns. We propose an alternative algorithm, dense network sub-grouping, which identifies dense groups of co-cited references. We demonstrate the algorithm using a data set from the field of family business research and compare it to two alternative methods, multidimensional scaling and clustering. We also introduce a free software tool, Sitkis, that implements the algorithm and other common bibliometric methods. The software identifies journal-, country- and university-specific citation patterns and co-citation groups, enabling the identification of ?invisible colleges.?
%0 Journal Article
%1 schildt_dense_2006
%A Schildt, Henri A
%A Mattsson, Juha T
%D 2006
%J Scientometrics
%K imported
%P 143-163
%T A dense network sub-grouping algorithm for co-citation analysis and its implementation in the software tool Sitkis
%U http://dx.doi.org/10.1007/s11192-006-0054-8
%V 67
%X Summary Clustering algorithms are used prominently in co-citation analysis by analysts aiming to reveal research streams within a field. However, clustering of widely cited articles is not robust to small variations in citation patterns. We propose an alternative algorithm, dense network sub-grouping, which identifies dense groups of co-cited references. We demonstrate the algorithm using a data set from the field of family business research and compare it to two alternative methods, multidimensional scaling and clustering. We also introduce a free software tool, Sitkis, that implements the algorithm and other common bibliometric methods. The software identifies journal-, country- and university-specific citation patterns and co-citation groups, enabling the identification of ?invisible colleges.?
@article{schildt_dense_2006,
abstract = {Summary\ \ Clustering algorithms are used prominently in co-citation analysis by analysts aiming to reveal research streams within a field. However, clustering of widely cited articles is not robust to small variations in citation patterns. We propose an alternative algorithm, dense network sub-grouping, which identifies dense groups of co-cited references. We demonstrate the algorithm using a data set from the field of family business research and compare it to two alternative methods, multidimensional scaling and clustering. We also introduce a free software tool, Sitkis, that implements the algorithm and other common bibliometric methods. The software identifies journal-, country- and university-specific citation patterns and co-citation groups, enabling the identification of ?invisible colleges.?},
added-at = {2007-12-03T17:48:07.000+0100},
author = {Schildt, Henri A and Mattsson, Juha T},
biburl = {https://www.bibsonomy.org/bibtex/2b576b9d4b7e6f585ec4561efcc2f0a1c/sercarfe},
interhash = {4c82e77fe90bee77e33a79d441eb0cb7},
intrahash = {b576b9d4b7e6f585ec4561efcc2f0a1c},
journal = {Scientometrics},
keywords = {imported},
pages = {143-163},
timestamp = {2007-12-03T17:48:23.000+0100},
title = {A dense network sub-grouping algorithm for co-citation analysis and its implementation in the software tool Sitkis},
url = {http://dx.doi.org/10.1007/s11192-006-0054-8 },
volume = 67,
year = 2006
}