document: Information genealogy : uncovering the flow of ideas in non-hyperlinked document databases We now have incrementally-grown databases of text documents ranging back for over a decade in areas ranging from personal email , to news-articles and conference proceedings . While accessing individual documents is easy , methods for overviewing and understanding these collections as a whole are lacking in number and in scope . In this paper , we address one such global analysis task , namely the problem of automatically uncovering how ideas spread through the collection over time . We refer to this problem as Information Genealogy . In contrast to bibliometric methods that are limited to collections with explicit citation structure , we investigate content-based methods requiring only the text and timestamps of the documents . In particular , we propose a language-modeling approach and a likelihood ratio test to detect influence between documents in a statistically well-founded way . Furthermore , we show how this method can be used to infer citation graphs and to identify the most influential documents in the collection . Experiments on the NIPS conference proceedings and the Physics ArXiv show that our method is more effective than methods based on document similarity .
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