IDEAS home Printed from https://ideas.repec.org/a/igg/jdsst0/v14y2022i1p1-19.html
   My bibliography  Save this article

Infgraph: Influential Researcher and Cited Research Analysis Using Citation Network

Author

Listed:
  • M. Geetha. (4c5df6a5-2de1-4fe2-931e-2244dd9617aa

    (Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences Chennai, Tamilnadu, India)

  • K. Suresh Kumar (5a673c34-1198-4f66-bcca-a6ed0f148b21

    (Department of Information Technology, Saveetha Engineering College, Chennai, Tamilnadu, India)

  • Ch. Vidyadhari (156ea7f4-c4a4-4b02-b555-4168eea8b781

    (Department of Information Technology, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, India)

  • R. Ganeshan (64a4b47c-93f1-4916-94eb-736ce70b1e60

    (School of Computer Science and Engineering, VIT Bhopal University, Madhya Pradesh, India)

Abstract

The understanding of references in research articles is essential for performing effectual research. This paper devises a hybrid model to find the influential cited paper and influential researchers from Web of Science (WOS) data. For determining the influential researcher, a series of steps is performed. Then the co-citation is performed for providing author-author co-relation that predicts the next co-author. Thereafter, visualization of the network is performed for research communication amongst different authors. Then, the network density is computed. Finally, the cluster coefficient is adapted for finding the influential researcher. Concurrently, for discovering influential cited papers, the pre-processing is performed using the stop word removal and stemming process. Then, the word2vec model is utilized for training the model to forecast the suitable word that comes next. Finally, the modified word mover's distance (MWMD) is utilized for determining the semantic similarity in order to discover influential cited papers.

Suggested Citation

  • M. Geetha. (4c5df6a5-2de1-4fe2-931e-2244dd9617aa & K. Suresh Kumar (5a673c34-1198-4f66-bcca-a6ed0f148b21 & Ch. Vidyadhari (156ea7f4-c4a4-4b02-b555-4168eea8b781 & R. Ganeshan (64a4b47c-93f1-4916-94eb-7, 2022. "Infgraph: Influential Researcher and Cited Research Analysis Using Citation Network," International Journal of Decision Support System Technology (IJDSST), IGI Global, vol. 14(1), pages 1-19, January.
  • Handle: RePEc:igg:jdsst0:v:14:y:2022:i:1:p:1-19
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDSST.311065
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jdsst0:v:14:y:2022:i:1:p:1-19. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.