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.
%0 Conference Paper
%1 Dietz:2007
%A Dietz, Laura
%A Bickel, Steffen
%A Scheffer, Tobias
%B Proceedings of the 24th international conference on Machine learning
%C New York, NY, USA
%D 2007
%I ACM
%K citation given models network seminar topic ws2012
%P 233--240
%R 10.1145/1273496.1273526
%T Unsupervised prediction of citation influences
%U http://doi.acm.org/10.1145/1273496.1273526
%X 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.
%@ 978-1-59593-793-3
@inproceedings{Dietz:2007,
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.},
acmid = {1273526},
added-at = {2012-09-19T15:52:59.000+0200},
address = {New York, NY, USA},
author = {Dietz, Laura and Bickel, Steffen and Scheffer, Tobias},
biburl = {https://www.bibsonomy.org/bibtex/2936ac83d3aeb948163d26fb289a0af4a/schwemmlein},
booktitle = {Proceedings of the 24th international conference on Machine learning},
description = {Unsupervised prediction of citation influences},
doi = {10.1145/1273496.1273526},
interhash = {ab17d1a9da7894b7ba7e52c390449455},
intrahash = {936ac83d3aeb948163d26fb289a0af4a},
isbn = {978-1-59593-793-3},
keywords = {citation given models network seminar topic ws2012},
location = {Corvalis, Oregon},
numpages = {8},
pages = {233--240},
publisher = {ACM},
series = {ICML '07},
timestamp = {2012-11-26T14:13:54.000+0100},
title = {Unsupervised prediction of citation influences},
url = {http://doi.acm.org/10.1145/1273496.1273526},
year = 2007
}