Abstract
This article describes the Blockmap, which is a mechanism for displaying and exploring network datasets. The data are presented in a squarified-mosaic form, which is well-suited for visual display on a computer or phone screen. The relational data are dimension-reduced and structured for interactive, hierarchical exploration. The Blockmap applies a combination of treemap and heatmap display schemes specifically to the analysis of large network datasets. The Blockmap offers the analyst a way to explore underlying node-level data, at the full-network level, according to shared characteristics of the constituent nodes. It offers a technique for exploring nodesets—collections of network nodes—which have been classified according to a user-defined set of rules or discriminative algorithms. Typically, nodes can be classified according to their common attributes or a stratification of their ego-level network measures, but means can be extended. Using a Blockmap, an analyst can profile a network according to the meaningful characteristics exhibited by the mosaic; this technique also offers theorists a platform for developing a methodological and analytic framework for characterizing and analyzing network data. Production versions of Blockmap technology are presently hosted in client- and web-based software and is available freely in *ORA-LITE.






















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Frantz, T.L. Blockmap: an interactive visualization tool for big-data networks. Comput Math Organ Theory 24, 149–168 (2018). https://doi.org/10.1007/s10588-017-9252-6
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DOI: https://doi.org/10.1007/s10588-017-9252-6