The Structure and Dynamics of Scientific Knowledge - Mapping Scientific Frontiers: The Quest for Knowledge Visualization - page 181

Fig. 5.12 A two-dimensional Pathfinder network integrated with information on term frequencies
as the third dimension (Reproduced from Chen 1998 )
Since 1997, I started to explore Pathfinder network scaling as a vehicle to
visualize complex networks (Chen 1997b , 1998 ). See Figs. 5.12 , 5.13 ,and 5.14 .
Pathfinder network scaling filters out excessive links in a network while maintaining
the salient structure of the network, more precisely, by preserving links that satisfy
the triangular inequality throughout the network. In 1999, I published a study
of author co-citation networks using Pathfinder network scaling techniques and
demonstrated the advantages of using Pathfinder over multidimensional scaling
because Pathfinder networks display connections explicitly and preserve salient
structure while pruning excessive links (Chen 1999 ).
In 2003, Howard White revisited the same dataset used in their 1998 author
co-citation analysis and applied the Pathfinder network techniques to represent
co-cited authors. He concluded that Pathfinder networks provide considerable
advantages over MDS maps because Pathfinder networks make the connections
explicit. Figure 5.15 shows a Pathfinder of 121 information science authors based
on raw co-citation counts. Garfield, Lancaster, and Salton are the most prominent
authors in the Pathfinder network; each is surrounded by a large number of co-cited
authors.
White and McCain ( 1998 ) discussed some issues concerning detecting paradigm
shifts. They compared author co-citation networks over three consecutive periods
using INDSCAL. White and McCain's work is a significant step towards under-
standing how we may grasp the dynamic of a scientific community and track the
development of a discipline.