%0 Conference Paper %1 he2009detecting %A He, Qi %A Chen, Bi %A Pei, Jian %A Qiu, Baojun %A Mitra, Prasenjit %A Giles, Lee %B Proceedings of the 18th ACM conference on Information and knowledge management %C New York, NY, USA %D 2009 %I ACM %K analysis citation evolution publication science sota topic %P 957--966 %R 10.1145/1645953.1646076 %T Detecting topic evolution in scientific literature: how can citations help? %U http://doi.acm.org/10.1145/1645953.1646076 %X Understanding how topics in scientific literature evolve is an interesting and important problem. Previous work simply models each paper as a bag of words and also considers the impact of authors. However, the impact of one document on another as captured by citations, one important inherent element in scientific literature, has not been considered. In this paper, we address the problem of understanding topic evolution by leveraging citations, and develop citation-aware approaches. We propose an iterative topic evolution learning framework by adapting the Latent Dirichlet Allocation model to the citation network and develop a novel inheritance topic model. We evaluate the effectiveness and efficiency of our approaches and compare with the state of the art approaches on a large collection of more than 650,000 research papers in the last 16 years and the citation network enabled by CiteSeerX. The results clearly show that citations can help to understand topic evolution better. %@ 978-1-60558-512-3

@inproceedings{he2009detecting, abstract = {Understanding how topics in scientific literature evolve is an interesting and important problem. Previous work simply models each paper as a bag of words and also considers the impact of authors. However, the impact of one document on another as captured by citations, one important inherent element in scientific literature, has not been considered. In this paper, we address the problem of understanding topic evolution by leveraging citations, and develop citation-aware approaches. We propose an iterative topic evolution learning framework by adapting the Latent Dirichlet Allocation model to the citation network and develop a novel inheritance topic model. We evaluate the effectiveness and efficiency of our approaches and compare with the state of the art approaches on a large collection of more than 650,000 research papers in the last 16 years and the citation network enabled by CiteSeerX. The results clearly show that citations can help to understand topic evolution better.}, acmid = {1646076}, added-at = {2013-06-07T10:00:59.000+0200}, address = {New York, NY, USA}, author = {He, Qi and Chen, Bi and Pei, Jian and Qiu, Baojun and Mitra, Prasenjit and Giles, Lee}, biburl = {https://www.bibsonomy.org/bibtex/29ce09c344acabaf577b1921f2dd6e60d/jaeschke}, booktitle = {Proceedings of the 18th ACM conference on Information and knowledge management}, doi = {10.1145/1645953.1646076}, interhash = {c185274210ab9b5d8dbc8872ebaa2eb0}, intrahash = {9ce09c344acabaf577b1921f2dd6e60d}, isbn = {978-1-60558-512-3}, keywords = {analysis citation evolution publication science sota topic}, location = {Hong Kong, China}, numpages = {10}, pages = {957--966}, publisher = {ACM}, timestamp = {2014-07-28T15:57:31.000+0200}, title = {Detecting topic evolution in scientific literature: how can citations help?}, url = {http://doi.acm.org/10.1145/1645953.1646076}, year = 2009 }