poster

Understanding evolution of research themes: a probabilistic generative model for citations

Authors:

Chengxiang Zhai,

Dan RothAuthors Info & Claims

KDD '13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 1115 - 1123

https://doi.org/10.1145/2487575.2487698

Published: 11 August 2013 Publication History

Abstract

Understanding how research themes evolve over time in a research community is useful in many ways (e.g., revealing important milestones and discovering emerging major research trends). In this paper, we propose a novel way of analyzing literature citation to explore the research topics and the theme evolution by modeling article citation relations with a probabilistic generative model. The key idea is to represent a research paper by a ``bag of citations'' and model such a ``citation document'' with a probabilistic topic model. We explore the extension of a particular topic model, i.e., Latent Dirichlet Allocation~(LDA), for citation analysis, and show that such a Citation-LDA can facilitate discovering of individual research topics as well as the theme evolution from multiple related topics, both of which in turn lead to the construction of evolution graphs for characterizing research themes. We test the proposed citation-LDA on two datasets: the ACL Anthology Network(AAN) of natural language research literatures and PubMed Central(PMC) archive of biomedical and life sciences literatures, and demonstrate that Citation-LDA can effectively discover the evolution of research themes, with better formed topics than (conventional) Content-LDA.

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https://doi.org/10.1007/s10462-024-11011-x
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https://dl.acm.org/doi/10.1145/3627673.3679597
Li HYu MLi XZhang JLi SLei JHuang H(2023)Probability-Distribution-Guided Adversarial Sample Attacks for Boosting Transferability and InterpretabilityMathematics10.3390/math1113301511:13(3015)Online publication date: 6-Jul-2023
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Understanding evolution of research themes: a probabilistic generative model for citations
1. Information systems

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cover image ACM Conferences

KDD '13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2013

1534 pages

ISBN:9781450321747

DOI:10.1145/2487575

Editors:
Rayid Ghani
University of Chicago
,
Ted E. Senator
SAIC
,
Paul Bradley
MethodCare, Inc.
,
Rajesh Parekh
Groupon
,
Jingrui He
Stevens Institute of Technology
,
General Chairs:
Robert L. Grossman
University of Chicago and Open Data Group
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Ramasamy Uthurusamy
General Motors Corporation (retired)
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Program Chairs:
Inderjit S. Dhillon
University of Texas
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Yehuda Koren
Google

Copyright © 2013 ACM.

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Published: 11 August 2013

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KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 11 - 14, 2013

Illinois, Chicago, USA

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KDD '13 Paper Acceptance Rate 125 of 726 submissions, 17%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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https://doi.org/10.1007/s10462-024-11011-x
Sun JHe YXu YSun JSun GSerra ESpezzano F(2024)A Learning-path based Supervised Method for Concept Prerequisite Relations Extraction in Educational DataProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679597(2168-2177)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679597
Li HYu MLi XZhang JLi SLei JHuang H(2023)Probability-Distribution-Guided Adversarial Sample Attacks for Boosting Transferability and InterpretabilityMathematics10.3390/math1113301511:13(3015)Online publication date: 6-Jul-2023
https://doi.org/10.3390/math11133015
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Wang JXu JChen WZhao L(2022)When Research Topic Trend Prediction Meets Fact-Based AnnotationsData Science and Engineering10.1007/s41019-022-00197-17:4(316-327)Online publication date: 12-Oct-2022
https://doi.org/10.1007/s41019-022-00197-1
Gao QHuang XDong KLiang ZWu J(2022)Semantic-enhanced topic evolution analysis: a combination of the dynamic topic model and word2vecScientometrics10.1007/s11192-022-04275-z127:3(1543-1563)Online publication date: 5-Feb-2022
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Khakimova AYang XZolotarev OBerberova MCharnine M(2020)Tracking Knowledge Evolution Based on the Terminology Dynamics in 4P-MedicineInternational Journal of Environmental Research and Public Health10.3390/ijerph1720744417:20(7444)Online publication date: 13-Oct-2020
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Chen XHan T(2020)A Micro Perspective of Research Dynamics Through “Citations of Citations” Topic AnalysisJournal of Data and Information Science10.2478/jdis-2020-00345:4(19-34)Online publication date: 28-Jul-2020
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