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On the Normalization and Visualization of Author Co-Citation Data
Click here for PDF <http://www.leydesdorff.net/aca07/aca07.pdf>
The debate about which similarity measure one should use for the
normalization in the case of Author Co-citation Analysis (ACA) is further
complicated when one distinguishes between the symmetrical co-citation--or,
more generally, co-occurrence--matrix and the underlying asymmetrical
citation--occurrence--matrix. In the Web environment, the approach of
retrieving original citation data and then using Salton's cosine or the
Pearson correlation to construct a similarity matrix is often not feasible.
In that case, one should use the Jaccard index, but preferentially after
adding the number of total citations (occurrences) on the main diagonal.
Unlike Salton's cosine and the Pearson correlation, the Jaccard index
abstracts from the distribution and focuses only on the intersection and the
sum of the two sets. Since the distributions in the co-occurrence matrix may
partially be based on spurious correlations, this property of the Jaccard
index can be considered as an advantage in this case. The argument is
illustrated with empirical data.
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