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Conference Paper

Unsupervised Prediction of Citation Influences

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44319

Dietz,  Laura
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44132

Bickel,  Steffen
Machine Learning, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45377

Scheffer,  Tobias
Machine Learning, MPI for Informatics, Max Planck Society;

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Citation

Dietz, L., Bickel, S., & Scheffer, T. (2007). Unsupervised Prediction of Citation Influences. In Z. Ghahramani (Ed.), ICML'07 (pp. 233-240). New York, NY: ACM. doi:10.1145/1273496.1273526.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-2126-1
Abstract
Abstract 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.