From Graphs to Maps

out of large data sets. However, traditional visualization methods
often fail to capture the underlying structural information,
clustering, and neighborhoods. Our algorithm for visualizing
relational data as a map provides a way to overcome some of the
shortcomings with the help of the geographic map metaphor. While
graphs, charts, and tables often require considerable effort to
comprehend, a map representation is more intuitive, as most people are
very familiar with maps and even enjoy carefully examining maps. The
effectiveness of the map representation algorithm is illustrated with
applications in recommendation systems for TV shows, movies, books,
and music. Several interesting and challenging geometric and graph
theoretic problems underlie this approach of creating maps from
graphs. Specifically, recent progress on contact representations,
rectilinear cartograms, and maximum differential coloring will be
briefly discussed.