Publication: How Do You Visualize a Million Links?
All || By Area || By Year| Title | How Do You Visualize a Million Links? | Authors/Editors* | Brown, Susan, Antoniuk, Jeffery, Bauer, Michael, Berberich, Jennifer, Radzikowska, Milena, Ruecker, Stan, Yung, Terence |
|---|---|
| Where published* | DH2010 Abstracts |
| How published* | Proceedings |
| Year* | 2010 |
| Volume | |
| Number | |
| Pages | 105-107 |
| Publisher | Alliance of Digital Humanities Organizations/King's College London |
| Keywords | visualization, social networking, humanities, literary history |
| Link | http://dh2010.cch.kcl.ac.uk/academic-programme/abstracts.html |
| Abstract |
In the past quarter century, established methods of literary history have been severely contested. On the one hand, syncretic, single-author histories have become problematic as a result of a combination of the expanded literary canon and a range of theoretical challenges. On the other, a demand for historicized overviews that reflect the radical recent reshaping in all fields of literary study has produced large numbers of both collectively written histories and encyclopedias or companions. Literary history thus tends towards compilations in which specialists treat their particular fields, at the cost of integration or of coherence. Meanwhile, the primary materials are increasingly available in digital form, and literary historical scholarship itself is increasingly produced digitally, whether as versions of established forms such as journal articles, or in resources that invoke the potential for new kinds of analysis. Major digital initiatives over the past decades have focused almost exclusively on digital resource creation: the increasingly pressing question is how to use this expanding body of materials to its fullest potential. [...] Brown and Bauer have been working on a visualization tool that illustrates the challenges facing the project of representing all of Orlandoâs semantically interrelated data through a graphical representation based on nodes and edges. It highlights the difficulty of providing prospect when dealing with a large and complexly structured data set, since the full set of relationships even of a moderate subset of the 1200 writers becomes unreadable, with over 16 million edges in the graph. We have done some work to explore algorithms and interfaces to accommodate these large data spaces and multitudes of relationships and tags. The challenge is to provide the researcher with a means of perceiving or specifying subsets of data, extracting the relevant information, building the nodes and edges, and then providing means to navigate the vast number of nodes and edges, especially given the limited amount of space on a computer monitor. The nodes (at the centre of the starbursts) represent writers, while the blue dots show other individuals. The edges are shown as differently colored lines indicating different kinds of relationships as determined by tags (identified in the colored boxes). A researcher can display names, hide certain edges by deselecting tags, and zoom in and move around a large graph of nodes and edges. The tool is a starting point for evaluating existing computational approaches and graphical displays of relationships as a means of exploring literary questions. It raises exciting questions regarding the integration of data mining approaches with a graphical interface, particularly for scholars suspicious of abstractions. Computationally, the question of how to make such a tool accessible to remote users is a challenge. |
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