Published in Proc. IEEE Conference on Social Computing, IEEE Press, Piscataway, NJ (October 2011).
Communities in social networks emerge from
interactions among individuals and can be analyzed through a
combination of clustering and graph layout algorithms. These
approaches result in 2D or 3D visualizations of clustered
graphs, with groups of vertices representing individuals that
form a community. However, in many instances the vertices
have attributes that divide individuals into distinct categories
such as gender, profession, geographic location, and similar. It
is often important to investigate what categories of individuals
comprise each community and vice-versa, how the community
structures associate the individuals from the same category.
Currently, there are no effective methods for analyzing both
the community structure and the category-based partitions of
social graphs. We propose Group-In-a-Box (GIB), a metalayout
for clustered graphs that enables multi-faceted analysis
of networks. It uses the treemap space filling technique to
display each graph cluster or category group within its own
box, sized according to the number of vertices therein. GIB
optimizes visualization of the network sub-graphs, providing a
semantic substrate for category-based and cluster-based
partitions of social graphs. We illustrate the application of GIB
to multi-faceted analysis of real social networks and discuss
desirable properties of GIB using synthetic datasets.