![]() For these reasons, this tutorial focuses as much on the principles behind creating, visualizing, and analyzing temporal networks (the “why”) as it does on the particular technical means by which we achieve these goals (the “how”). With the rate at which network analysis is developing, there will soon be more user friendly ways to produce similar visualizations and analyses, as well as entirely new metrics of interest. This tutorial introduces methods for visualizing and analyzing temporal networks using several libraries written for the statistical programming language R. Temporal Network Analysis is still a pretty new approach in fields outside epidemiology and social network analysis. Temporal Network Analysis, also known as Temporal Social Network Analysis (TSNA), or Dynamic Network Analysis (DNA), might be just what you’re looking for. Wouldn’t it be great if you could reflect these changes and developments in your visualization and analysis of a network? People or things may occupy highly central roles for a brief period of time – perhaps a day, a year, or a decade – but they rarely begin or end their existence within a network in such positions. Actors or objects enter and exit these networks over the course of their existence. At certain points they may grow, or shrink, or dissolve completely. The reality is that most historical networks change over time. Perhaps the network looks larger and more robust than it felt to you as you were piecing it together from your archival sources, or perhaps the centrality measurements of your nodes don’t make much sense when you think about the actual historical circumstances in which they existed. You have probably visualized your static network, and analyzed it, but you may still feel that something is missing or not quite right. Or perhaps you are a historian of art studying a network made up of print designers and engravers, with connections derived from their collaborations on prints. Perhaps you are a historian of religion researching Quaker correspondence networks, in which the nodes represent writers and recipients of letters, and the edges of a network represent epistolary exchanges. If you are reading this tutorial, you might already have some experience modeling humanities data as a network. Thinking Temporally: Forward Reachable Sets.Beyond the Hairball: Dynamic Network Metrics.Making the Hard Choices: Translating Historical Data into TNA Data.
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