An amoeboid approach for identifying optimal citation flow in big scholarly data network. (16th December 2018)

• Record Type: Journal Article
• Authors: J P, Nivash; L D, Dhinesh Babu
• Other Names: Krishna P. Venkata guestEditor.; Yenduri Sumanth guestEditor.; Ariwa Ezendu guestEditor.
• Is Part Of: International journal of communication systems. Volume 33:Number 13(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-12-16
• Subjects: academic social networks -- big scholarly data -- citation network -- expansion contribution -- scientific collaboration network; Telecommunication systems -- Periodicals; 621.382
• DOI: 10.1002/dac.3874
• Languages: English
• ISSNs: 1074-5351

Abstract

Summary: Scholarly big data network is a complex network of citations from research community across the globe. An effective scholar assessment structure is essential for scholars, researchers, and universities. The research publications are an important factor in the university rankings. The fast growth of digital publishing and scholarly data is progressively challenging every day. These days, the scholarly data can be accessed effortlessly through various data analysis techniques. In this paper, a new framework is designed for big scholarly data, and an amoeboid approach article‐optimal citation flow (A‐OCF) is used to find the optimal flow of citations in the big scholarly data network. A novel modern metrics for article quality (MMAQ) metric is proposed to identify the quality of articles. The performance analysis uses different bibliometric measures, including the impact factor citations, conference proceedings citations, and other citations with the purpose of measuring the quality of cited articles. The scholar analytic results are equated with existing techniques. We have also analyzed central articles in a research area through the MMAQ metrics and tested it with benchmark data sets. Abstract : The fast growth of digital publishing and scholarly data is progressively challenging every day, and the scholarly data can be accessed effortlessly through various data analysis techniques. In this paper, a new framework is designed for big scholarly data, and an amoeboid approach article‐optimal citation flow (A‐OCF) is used to find the optimal flow of citations in the big scholarly data network. A novel modern metrics for article quality (MMAQ) metric is proposed to identify the quality of articles.