PageRank variants in the evaluation of citation networks

Nykl, Michal; Jezek, Kamil; Fiala, Dalibor; Dostál, Martin

doi:10.1016/j.joi.2014.06.005

Cited by 53 publications

(19 citation statements)

References 13 publications

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“…The PageRank algorithm and its variants were used for the assessment of various types of citation-publication networks. The question has been raised whether better assessment results were depended directly on an author network or on a publication-citation network [20].…”

Section: Global Citation Impact (Gci)mentioning

confidence: 99%

Global Citation Impact rather than Citation Count

Chughtai¹,

Lee²,

khan³

et al. 2018

ijacsa

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Abstract-The progressing bloom in the tome of scientific literature available today debars researchers from efficiently shrewd the relevant from irrelevant content. Researchers are persistently engrossed in impactful papers, authors, and venues in their respective fields. Impact of an article depends on the citation received but just a citation count can't give readers indepth information about the article. That is the reason some articles are quantified unfairly on the basis of a citation count. In this paper, Global Citation Impact (GCI) is proposed which addresses the issue of considering citations of papers equally. Intuitively, the papers citing a paper are not of the same worth. The proposed index not only considers the number of citations (popularity) like many existing methods did but also considers the worth of citations (prestige). Results and discussions show that researcher whose work is cited by other prestigious papers gets higher rank which is quite fair crediting for research impact.

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Section: Global Citation Impact (Gci)mentioning

confidence: 99%

Global Citation Impact rather than Citation Count

Chughtai¹,

Lee²,

khan³

et al. 2018

ijacsa

View full text Add to dashboard Cite

show abstract

“…Nykl et al [2014]; Fiala et al [2015a]). Usually a decaying weighting scheme is introduced, in order to increasingly penalise older edges/nodes and favour recent ones (see inter alia Xing and Ghorbani [2004]; Ding [2011]; ; Fiala et al [2015b]; Kong et al [2015], for a review [Liao et al, 2017, 4.2]).…”

Section: Related Workmentioning

confidence: 99%

TimeRank: A dynamic approach to rate scholars using citations

Franceschet

Colavizza

2017

Journal of Informetrics

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Rating has become a common practice of modern science. No rating system can be considered as final, but instead several approaches can be taken, which magnify different aspects of the fabric of science. We introduce an approach for rating scholars which uses citations in a dynamic fashion, allocating ratings by considering the relative position of two authors at the time of the citation among them. Our main goal is to introduce the notion of citation timing as a complement to the usual suspects of popularity and prestige. We aim to produce a rating able to account for a variety of interesting phenomena, such as positioning raising stars on a more even footing with established researchers. We apply our method on the bibliometrics community using data from the Web of Science from 2000 to 2016, showing how the dynamic method is more effective than alternatives in this respect.

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“…Ding et al [22] observed the effects of different damping factors (ranging from 0.05 to 0.95) for author co-citation network, and noted that citation rank is close to PageRank for damping factor of 0.55. A detailed study on PageRank variants for ranking authors may be found in the work by Michal Nykl et al [39].…”

Section: Related Workmentioning

confidence: 99%

$$C^3$$ C 3 -index: a PageRank based multi-faceted metric for authors’ performance measurement

et al. 2016

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Ranking scientific authors is an important but challenging task, mostly due to the dynamic nature of the evolving scientific publications. The basic indicators of an author's productivity and impact are still the number of publications and the citation count (leading to the popular metrics such as h-index, g-index etc.). H-index and its popular variants are mostly effective in ranking highly-cited authors, thus fail to resolve ties while ranking mediumcited and low-cited authors who are majority in number. Therefore, these metrics are inefficient to predict the ability of promising young researchers at the beginning of their career. In this paper, we propose C 3 -index that combines the effect of citations and collaborations of an author in a systematic way using a weighted multi-layered network to rank authors. We conduct our experiments on a massive publication dataset of Computer Science and show that -(i) C 3index is consistent over time, which is one of the fundamental characteristics of a ranking metric, (ii) C 3 -index is as efficient as h-index and its variants to rank highly-cited authors, (iii) C 3 -index can act as a conflict resolution metric to break ties in the ranking of medium-cited and low-cited authors, (iv) C 3index can also be used to predict future achievers at the early stage of their career.

show abstract

PageRank variants in the evaluation of citation networks

Cited by 53 publications

References 13 publications

Global Citation Impact rather than Citation Count

Global Citation Impact rather than Citation Count

TimeRank: A dynamic approach to rate scholars using citations

$$C^3$$ C 3 -index: a PageRank based multi-faceted metric for authors’ performance measurement

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