期刊
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
卷 17, 期 12, 页码 2283-2290出版社
IEEE COMPUTER SOC
DOI: 10.1109/TVCG.2011.169
关键词
Network Visualization; Social Networks; Time Series Data; Visual Knowledge Discovery and Representation; Glyph-based Techniques
In modeling and analysis of longitudinal social networks, visual exploration is used in particular to complement and inform other methods. The most common graphical representations for this purpose appear to be animations and small multiples of intermediate states, depending on the type of media available. We present an alternative approach based on matrix representation of gestaltlines (a combination of Tufte's sparklines with glyphs based on gestalt theory). As a result, we obtain static, compact, yet data-rich diagrams that support specifically the exploration of evolving dyadic relations and persistent group structure, although at the expense of cross-sectional network views and indirect linkages.
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