  Co-occurrence matrices, such as co-citation, co-word, and co-link matrices,
have been used widely in the information sciences. However, confusion and
controversy have hindered the proper statistical analysis of this data. The
underlying problem, in our opinion, involved understanding the nature of
various types of matrices. This paper discusses the difference between a
symmetrical co-citation matrix and an asymmetrical citation matrix as well as
the appropriate statistical techniques that can be applied to each of these
matrices, respectively. Similarity measures (like the Pearson correlation
coefficient or the cosine) should not be applied to the symmetrical co-citation
matrix, but can be applied to the asymmetrical citation matrix to derive the
proximity matrix. The argument is illustrated with examples. The study then
extends the application of co-occurrence matrices to the Web environment where
the nature of the available data and thus data collection methods are different
from those of traditional databases such as the Science Citation Index. A set
of data collected with the Google Scholar search engine is analyzed using both
the traditional methods of multivariate analysis and the new visualization
software Pajek that is based on social network analysis and graph theory.
