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This paper proposes a scheme of detecting topic evolutions in bibliographic databases. There have been a lot of scientific bibliographies, such as DBLP, CiteSeerX, MEDLINE/PubMed, ADS, arXiv, etc., and hence it has been extremely important to extract useful information from these databases. It should be noticed that, in such databases, citations play crucial role to represent relationships among different publications. To make the best use of citation information as well as textual features for extracting topic evolutions in a bibliographic database, we propose a scheme based on non-negative matrix factorization (NMF). More precisely, we first partition the set of publications in a database according to their publication years, and apply NMF to extract clusters of publications. Notice that we take into account citation information to perform NMF for better clustering. Having obtained sets of publications for each time span, we associate similar clusters in consecutive time spans according to their similarity. Thus we can obtain time evolution of topics and clusters of publications. In the experiments we demonstrate the proposed scheme can successfully extract topic evolutions in real bibliographic databases, CiteSeerX and arXiv.
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