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Uncovering research trends and topics of communities in machine learning

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Abstract

This paper aims to uncover the research topics in machine learning research communities in a scientific collaboration network (SCN) to enhance the characteristic of systems such as retrieval or recommendation in intelligence-based systems. The existing research mainly focuses on the community evolution and measurement of typical features of the network. It is however unexplored how to identify the research interest of the communities along with authors in each community. A dataset is prepared consisting of 21,906 scientific articles from six top journals in the field of machine learning published from 1988 to 2017. An integrated approach combining the author-topic (AT) model with communities using through the directed affiliations (CoDA) method is explored to identify the research interest of the communities in a scientific collaboration network. The top rank communities are identified using the crank network community prioritization method. Finally, the similarity and dissimilarity of research interest in communities across decades are uncovered using the cosine similarity. The experimental results demonstrate the effectiveness and efficacy of the proposed technique. This study may be helpful for upcoming researchers to explore the research trends and topics in machine learning research communities.

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Authors and Affiliations

  1. Department of Computer Engineering, Netaji Subash Institute of Technology, University of Delhi, Sector-3, Dwarka, Delhi, 110078, India

    Deepak Sharma & Bijendra Kumar

  2. School of Computer & Systems Sciences, Jawaharlal Nehru University, Delhi, 110067, India

    Satish Chand

  3. Multimodal Digital Media Analysis Lab, Indraprastha Institute of Information Technology, Delhi, 110020, India

    Rajiv Ratn Shah

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Sharma, D., Kumar, B., Chand, S. et al. Uncovering research trends and topics of communities in machine learning. Multimed Tools Appl 80, 9281–9314 (2021). https://doi.org/10.1007/s11042-020-10072-8

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