Probabilistic author-topic models for information discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering scientific literature using sparse citation graph analysis
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Web document clustering using hyperlink structures
Computational Statistics & Data Analysis
Detecting topic evolution in scientific literature: how can citations help?
Proceedings of the 18th ACM conference on Information and knowledge management
Using semi-structured data for assessing research paper similarity
Information Sciences: an International Journal
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We propose a generative model based on latent Dirichlet allocation for mining distinct topics in document collections by integrating the temporal ordering of documents into the generative process. The document collection is divided into time segments where the discovered topics in each segment is propagated to influence the topic discovery in the subsequent time segments. We conduct experiments on the collection of academic papers from CiteSeer repository. We augment the text corpus with the addition of user queries and tags and integrate the citation graph to boost the weight of the topical terms. The experiment results show that segmented topic model can effectively detect distinct topics and their evolution over time.