In a blog post at Modeling Literary History, entitled “Using Networks to Re-think Periodization,” Michael Gavin (University of South Carolina) considers the challenge of communicating more than the mere scope of a dataset with large-scale network visualizations, in this case using EEBO-TCP (Early English Books Online, Text Creation Partnership) data. After identifying and highlighting dense clusters in the “hairball” network visualization and stacking these clusters on a timeline, Gavin concludes:
Taken together, these graphs capture the reasons why periodization is tempting as a shorthand for historical difference, but they also stage the inadequacy of that shorthand. Each period appears clearly through the metadata’s noise as bursts of publishing activity. Yet, these bursts bear little resemblance to a neatly demarcated series of befores and afters. Literary periods are overlapping and thoroughly interpenetrating – not blocks of time neatly stacked next to each other from left to right like books on a shelf, but networks of human activity threaded into a complex fabric of temporality.
Gavin’s approach highlights the advantages of using mulitple visualizations to analyze historical data – the initial “giant hairball” model was too dense to provide much insight, but stacking that data on a timeline “reveals the many layers of temporality that unfold through print’s social space.”