  A multiple-perspective co-citation analysis method is introduced for
characterizing and interpreting the structure and dynamics of co-citation
clusters. The method facilitates analytic and sense making tasks by integrating
network visualization, spectral clustering, automatic cluster labeling, and
text summarization. Co-citation networks are decomposed into co-citation
clusters. The interpretation of these clusters is augmented by automatic
cluster labeling and summarization. The method focuses on the interrelations
between a co-citation cluster's members and their citers. The generic method is
applied to a three-part analysis of the field of Information Science as defined
by 12 journals published between 1996 and 2008: 1) a comparative author
co-citation analysis (ACA), 2) a progressive ACA of a time series of
co-citation networks, and 3) a progressive document co-citation analysis (DCA).
Results show that the multiple-perspective method increases the
interpretability and accountability of both ACA and DCA networks.
