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Trend Analysis of Machine Learning Research Using Topic Network Analysis

  • Deepak SharmaEmail author
  • Bijendra Kumar
  • Satish Chand
Conference paper
  • 1.2k Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 799)

Abstract

In this paper, a topic network analysis approach is proposed which integrates topic modeling and social network analysis. We collected 16,855 scientific papers from six top journals in the field of machine learning published from 1997 to 2016 and analyzed them with the topic network. The dataset is break down into 4 intervals to identify topic trends and performed the time-series analysis of topic network. Our experimental results show centralization of the topic network has the highest score from 2002 to 2006, and decreases for next 5 years and increases again. For last 5 years, centralization of the degree centrality and closeness centrality increases, while centralization of the betweenness centrality decreases again. Also, data analytic and computer vision are identified as the most interrelated topic among other topics. Topics with the highest degree centrality evolve component analysis, text mining, biometric and computer vision according to time. Our approach extracts the interrelationships of topics, which cannot be detected with conventional topic modeling approaches, and provides topical trends of machine learning research.

Keywords

Topic network analysis Social network analysis Topic modeling Latent Dirichlet Allocation Research trend analysis 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer EngineeringNetaji Subash Institute of TechnologyNew DelhiIndia
  2. 2.School of Computer and Systems SciencesJawaharlal Nehru UniversityNew DelhiIndia

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