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Collaboration Network Analysis Based on Normalized Citation Count and Eigenvector Centrality

Collaboration Network Analysis Based on Normalized Citation Count and Eigenvector Centrality

Anand Bihari, Sudhakar Tripathi, Akshay Deepak
Copyright: © 2019 |Volume: 6 |Issue: 1 |Pages: 12
ISSN: 2334-4598|EISSN: 2334-4601|EISBN13: 9781522568445|DOI: 10.4018/IJRSDA.2019010104
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MLA

Bihari, Anand, et al. "Collaboration Network Analysis Based on Normalized Citation Count and Eigenvector Centrality." IJRSDA vol.6, no.1 2019: pp.61-72. http://doi.org/10.4018/IJRSDA.2019010104

APA

Bihari, A., Tripathi, S., & Deepak, A. (2019). Collaboration Network Analysis Based on Normalized Citation Count and Eigenvector Centrality. International Journal of Rough Sets and Data Analysis (IJRSDA), 6(1), 61-72. http://doi.org/10.4018/IJRSDA.2019010104

Chicago

Bihari, Anand, Sudhakar Tripathi, and Akshay Deepak. "Collaboration Network Analysis Based on Normalized Citation Count and Eigenvector Centrality," International Journal of Rough Sets and Data Analysis (IJRSDA) 6, no.1: 61-72. http://doi.org/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.