Unsupervised prediction of citation influences
Published 20 June 2007
Laura Dietz, Steffen Bickel, Tobias Scheffer
Citations240
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TL;DR
A probabilistic topic model is devised that explains the generation of documents and incorporates the aspects of topical innovation and topical inheritance via citations, and its ability to predict the strength of influence of citations against manually rated citations is evaluated.
Abstract
Abstract Publication repositories contain an abundance of information about the \nevolution of scientific research areas. We address the problem of creating a \nvisualization of a research area that describes the flow of topics between \npapers, quantifies the impact that papers have on each other, and helps to \nidentify key contributions. To this end, we devise a probabilistic topic model \nthat explains the generation of documents; the model incorporates the aspects \nof topical innovation and topical inheritance via citations. We evaluate the \nmodel's ability to predict the strength of influence of citations against \nmanually rated citations.
Keywords
Computer SciencePhysics and Astronomy
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