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Collaboration Network Analysis Based on Normalized Citation Count and Eigenvector Centrality
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Author(s): Anand Bihari (National Institute of Technology Patna, Bihar, India), Sudhakar Tripathi (R. E. C. Ambedkar Nagar, Uttar Pradesh, India)and Akshay Deepak (National Institute of Technology Patna, Bihar, India)
Copyright: 2019
Volume: 6
Issue: 1
Pages: 12
Source title:
International Journal of Rough Sets and Data Analysis (IJRSDA)
Editor(s)-in-Chief: Parikshit Narendra Mahalle (Department of Artificial Intelligence and Data Science, Bansilal Ramnath Agarwal Charitable Trust's, Vishwakarma Institute of Information Technology, India)
DOI: 10.4018/IJRSDA.2019010104
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Abstract
In the research community, the estimation of the scholarly impact of an individual is based on either citation-based indicators or network centrality measures. The network-based centrality measures like degree, closeness, betweenness & eigenvector centrality and the citation-based indicators such as h-index, g-index & i10-index, etc., are used and all of the indicators give full credit to all of the authors of a particular article. This is although the contribution of the authors are different. To determine the actual contribution of an author in a particular article, we have applied arithmetic, geometric and harmonic counting methods for finding the actual contribution of an individual. To find the prominent actor in the network, we have applied eigenvector centrality. To authenticate the proposed analysis, an experimental study has been conducted on 186007 authors collaboration network, that is extracted from IEEE Xplore. The experimental results show that the geometric counting-based credit distribution among scholars gives better results than others.
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