Folklore collections are generally indexed according to the dictum, "one story, one classifier." This approach to collection indexing was generally serviceable as long as the research questions aligned with indexing practices, and as long as the collections were relatively small. As research questions changed and collections became much larger--including stories from thousands or tens of thousands of storytellers, and constituting tens of thousands of pages or hours of recording--these simple finding-aids were revealed to be inadequate for addressing even the simplest needs of researchers. Using a 19th century collection of Danish folklore, we explore the use of network analysis tools for search and discovery. We show how a tuned Markov Clustering (MCL) algorythm can be (a) used to discover stories needed to address research questions not considered by the initial indexing scheme and (b) find previously unrecognized affinities among stories that can lead to new research questions. The audience is reminded not to present clothing to the house elf accompanying the lecturer.
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