Graphics Reference
In-Depth Information
Note
Chapter 10, “Hierarchies,” covers visualization and analysis of business
hierarchies in depth.
Communities
Graphs are indispensable for revealing communities, which are
fundamental to understanding macro relationships and dynamics in
business data. Communities in a graph visualization are similar to
geospatial communities on a map in that they are qualitatively reflected
by clusters of related members in close proximity, distinguishable from the
field of other graph members.
Figure 2-7
shows communities of philosophers linked by influence using
data extracted from Wikipedia by DBpedia. The PageRank algorithm is
used to size nodes based on their degree of influence, and layouts are used
to cluster nodes with common influences. Even without knowing much
about philosophy, you can spot the most influential figures such as Kant,
Marx, and Wolff, as well as many of the ancients like Plato and Aristotle.
Communities of influence also have apparent regional tendencies, with a
prevalence of German names in the mid right and British names in several
clusters to the lower left.
Delving into the dynamics of influence is central to the art of persuasion
in business. The popular writer Malcom Gladwell puts forth a social theory
of influence in
The Tipping Point
(New York: Little, Brown, and Company,
2000), which suggests the importance of individuals he labels mavens and
connectors, as well as salesmen. Graph visualization and analytics like
PageRank and centrality algorithms can helpreveal mavens and connectors.
For example, a connector would be a hub reflected by many incoming and
outgoing connections, including bridge connections to other communities.
On the other hand, a maven might be more likely to show as a node with a
large number of influential outgoing links.
