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 familybusiness 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 schildt2006
%A Schildt, Henri A.
%A Mattsson, Juha T.
%D 2006
%J Scientometrics
%K co-citation netzwerke software
%N 1
%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 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 familybusiness 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{schildt2006,
abstract = {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 familybusiness 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 = {2009-11-03T12:58:12.000+0100},
author = {Schildt, Henri A. and Mattsson, Juha T.},
biburl = {https://www.bibsonomy.org/bibtex/254917f845e9c81debd023e0640e75762/wdees},
description = {SpringerLink - Zeitschriftenbeitrag},
interhash = {4c82e77fe90bee77e33a79d441eb0cb7},
intrahash = {54917f845e9c81debd023e0640e75762},
journal = {Scientometrics},
keywords = {co-citation netzwerke software},
number = 1,
pages = {143-163},
timestamp = {2009-11-03T12:58:12.000+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
}