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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Book ChapterDOI
Topic-Sensitive Influential Paper Discovery in Citation Network
TL;DR: This work creatively combine both topics of text and the influence of topics over citation networks to discover influential articles, and proposes MTID, a scalable generative model, which generates the network with these two parameters.
Journal ArticleDOI
The twenty-first century of structural engineering research: A topic modeling approach
TL;DR: In this article, the latent Dirichlet allocation (LDA) was applied to analyze 51,346 article abstracts from 23 prestigious journals in structural engineering with a publication period from 2000 to 2020.
Book ChapterDOI
ScienScan - an efficient visualization and browsing tool for academic search
TL;DR: ScienScan discovers topics in the search results and summarizes them in the form of a concise hierarchical topic map that informatively represents the results in a visual way and provides an additional filtering control.
Posted Content
Predicting Research Trends From Arxiv
TL;DR: This approach is bottom-up: it first rank papers by their normalized citation counts, then group top-ranked papers into different categories based on the tasks that they pursue and the methods they use, and finds that the dominating paradigm in cs.CL revolves around natural language generation problems and those incs.LG revolve around reinforcement learning and adversarial principles.
Journal ArticleDOI
Unsupervised analysis of top-k core members in poly-relational networks
TL;DR: An optimization framework to jointly deal with the two tasks by maximizing the connectivity between the candidates of the top-k core members across all relations with a synchronously updated weight for each relation is proposed.
References
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Proceedings Article
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