Proceedings ArticleDOI
Detecting topic evolution in scientific literature: how can citations help?
Qi He,Bi Chen,Jian Pei,Baojun Qiu,Prasenjit Mitra,C. Lee Giles +5 more
- pp 957-966
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TLDR
An iterative topic evolution learning framework is proposed by adapting the Latent Dirichlet Allocation model to the citation network and develop a novel inheritance topic model, which clearly shows that citations can help to understand topic evolution better.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.read more
Citations
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Journal ArticleDOI
Generation of topic evolution trees from heterogeneous bibliographic networks
TL;DR: It is shown that meta-paths over a heterogeneous bibliographic network (consisting of papers, authors and venues) can be used to identify the network elements that made the greatest contributions to a topic and that restricted networks provide more useful topic evolution trees.
Journal ArticleDOI
Dynamic Online HDP model for discovering evolutionary topics from Chinese social texts
TL;DR: Compared with other related topic models on Chinese social media dataset Tianya-80299, the experiment results show that DOHDP model provides the best performance for discovering the evolutionary topics of Chinese social texts.
Posted Content
A Historical Analysis of the Field of OR/MS using Topic Models
TL;DR: This study investigates the content of the published scientific literature in the fields of operations research and management science (OR/MS) since the early 1950s, using a topic model, using Latent Dirichlet Allocation (LDA) to reveal the temporal dynamics of the field, journals, and topics.
Journal ArticleDOI
Topic diffusion analysis of a weighted citation network in biomedical literature
Munui Kim,Injun Baek,Min Song +2 more
TL;DR: This study builds a citation network, filters it using citation influence values, and examines the diffusion of topics not only in the field but also in the subfields of p53 to measure the strength of citation influence and identify paper topics.
Proceedings Article
Forecasting emerging trends from scientific literature
TL;DR: This work forms a dataset by extracting the keywords from previous year proceedings and quantify their yearly relevance using tf-idf scores and contains ranked lists of related keywords and experts for each keyword.
References
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Proceedings Article
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Journal ArticleDOI
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