Unsupervised Prediction of Citation Influences

author: Laura Dietz, Department RG2: Machine Learning, Max Planck Institute for Computer Science, Max Planck Institute
published: June 23, 2007,   recorded: June 2007,   views: 7216


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Publication repositories contain an abundance of information about the evolution of scientific research areas. We address the problem of creating a visualization of a research area that describes the flow of topics between papers, quantifies the impact that papers have on each other, and helps to identify key contributions. To this end, we devise a probabilistic topic model that explains the generation of documents; the model incorporates the aspects of topical innovation and topical inheritance via citations. We evaluate the model's ability to predict the strength of influence of citations against manually rated citations.

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