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1111 An Experimental Comparison of Bibliometric Mapping Techniques Nees Jan van Eck, Ludo Waltman, Rommert Dekker Erasmus University Rotterdam, The Netherlands.

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Presentation on theme: "1111 An Experimental Comparison of Bibliometric Mapping Techniques Nees Jan van Eck, Ludo Waltman, Rommert Dekker Erasmus University Rotterdam, The Netherlands."— Presentation transcript:

1 1111 An Experimental Comparison of Bibliometric Mapping Techniques Nees Jan van Eck, Ludo Waltman, Rommert Dekker Erasmus University Rotterdam, The Netherlands {nvaneck,lwaltman,rdekker}@few.eur.nl Jan van den Berg Delft University of Technology, The Netherlands j.vandenberg@tudelft.nl 10th International Conference on Science and Technology Indicators Vienna, September 18, 2008 1

2 2 Bibliometric mapping Similarity measure DirectIndirect JaccardCosine Association strength … Pearson correlation Cosine… Unit of analysis Authors JournalsWords/terms Web pages… Mapping technique Distance basedGraph based MDSVxOrdVOS…Pajek Pathfinder networks …

3 3 Bibliometric mapping Similarity measure DirectIndirect JaccardCosine Association strength … Pearson correlation Cosine… Unit of analysis Authors JournalsWords/terms Web pages… Mapping technique Distance basedGraph based MDSVxOrdVOS…Pajek Pathfinder networks …

4 4 Data sets Similarity measure DirectIndirect JaccardCosine Association strength … Pearson correlation Cosine… Mapping technique Distance basedGraph based MDSVxOrdVOS…Pajek Pathfinder networks … 1.Journals in economics, management, and operations research  2001-2005  ISI categories: business; business, finance; economics; management; and operations research & management science  376 journals 2.Terms in computational intelligence  2001-2005  337 terms Unit of analysis Authors JournalsWords/terms Web pages…

5 Unit of analysis Authors JournalsWords/terms Web pages… 5 Similarity measures Similarity measure DirectIndirect JaccardCosine Association strength … Pearson correlation Cosine… Mapping technique Distance basedGraph based MDSVxOrdVOS…Pajek Pathfinder networks … Other names for the association strength  probabilistic affinity index  proximity index

6 Unit of analysis Authors JournalsWords/terms Web pages… 6 Mapping techniques Similarity measure DirectIndirect JaccardCosine Association strength … Pearson correlation Cosine… Mapping technique Distance basedGraph based MDSVxOrdVOS…Pajek Pathfinder networks … Ordinal MDS in SPSS PROXSCAL VxOrd (DrL)  VxOrd1: top 10 similarities, edge cutting (default settings)  VxOrd2: all similarities, no edge cutting  VxOrd3: top 10 similarities, no edge cutting

7 7 Evaluation criteria 1.Quality of the maps Does a map provide a satisfactory representation of the data? 2.Robustness Measures the similarity between maps that have been constructed based on different samples drawn randomly from the same data set 3.Efficiency Measures the similarity between a map that has been constructed based on an entire data set and maps that have been constructed based on samples drawn randomly from that data set

8 8 MDS – Jaccard index (journals)

9 9 VxOrd1 – cosine (journals)

10 10 VxOrd2 – cosine (journals)

11 11 VxOrd3 – cosine (journals)

12 12 VOS – association strength (journals)

13 13 Comparison of maps (journals) MDS clearly performs worst, with no good separation between journals from different fields As is typical for MDS, journals are located more or less uniformly distributed within a circle VxOrd3 and VOS perform quite well, with a clear clustering of journals based on their field

14 14 Robustness and efficiency (journals) MDS and VxOrd1 have a low robustness and efficiency The other techniques do much better

15 15 MDS – Jaccard index (terms)

16 16 VxOrd1 – cosine (terms)

17 17 VxOrd2 – cosine (terms)

18 18 VxOrd3 – cosine (terms)

19 19 VOS – association strength (terms)

20 20 Comparison of maps (terms)

21 21 Robustness and efficiency (terms)

22 22 Conclusions No complete specification of VxOrd is available in the literature MDSVxOrdVOS Quality of the maps locates objects more or less uniformly distributed within a circle, with no clearly separated clusters produces attractive maps with a very clear clustering, but only if appropriate parameter values are chosen produces satisfactory maps without the need to choose any parameter values Robustness and efficiency low depends on parameter values and data set high

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