Unsupervised Prediction of Citation Influences

Posted in Science on September 07, 2008

Unsupervised Prediction of Citation Influences

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.

Author: Laura Dietz, Max Planck Institut Für Informatik

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