Quantifying the influence of scientists and their publications: distinguishing between prestige and popularity

Zhou, Yanbo; Lü, Linyuan; Li, Menghui

doi:10.1088/1367-2630/14/3/033033

Cited by 91 publications

(70 citation statements)

References 36 publications

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“…In addition to co-authorship activities between editors, as shown above, we must also consider the influence of editorial glorification [37] by citations by a co-author who is not an editor: Author3.…”

Section: Author3mentioning

confidence: 99%

Quantitative and Qualitative Analysis of Editor Behavior through Potentially Coercive Citations

et al. 2017

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How much is the h-index of an editor of a well-ranked journal improved due to citations which occur after his/her appointment? Scientific recognition within academia is widely measured nowadays by the number of citations or h-index. Our dataset is based on a sample of four editors from a well-ranked journal (impact factor, IF, greater than 2). The target group consists of two editors who seem to benefit by their position through an increased citation number (and subsequently h-index) within the journal. The total amount of citations for the target group is greater than 600. The control group is formed by another set of two editors from the same journal whose relations between their positions and their citation records remain neutral. The total amount of citations for the control group is more than 1200. The timespan for which the citations' pattern has been studied is 1975-2015. Previous coercive citations for a journal's benefit (an increase of its IF) has been indicated. To the best of our knowledge, this is a pioneering work on coercive citations for personal (editors') benefit. Editorial teams should be aware about this type of potentially unethical behavior and act accordingly.

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Section: Author3mentioning

confidence: 99%

Quantitative and Qualitative Analysis of Editor Behavior through Potentially Coercive Citations

et al. 2017

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“…Besides co-authorship activities between editors, as shown above, we must also consider the influence of editorial glorification [35] by citations by a co-author who is not an editor: Author3. In addition, due to his/her co-authorship (with Author1 and Author2), he/she received another six citations in 2014 and 22 in 2015 in Journal1 (regardless of who authored them).…”

Section: Author3mentioning

confidence: 99%

Quantitative and Qualitative Analysis of Editor Behavior through maybe Coercive Citations

Herţeliu¹,

Ausloos²,

Ileanu³

et al. 2017

Preprint

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How much is the h-index of an editor of a well ranked journal improved due to citations which occur after his/her appointment? Scientific recognition within academia is widely measured nowadays by the number of citations or h-index. Our dataset is based on a sample of four editors from a well ranked journal (impact factor -IF -greater than 2). The target group consists of two editors who seem to benefit by their position through an increased citation number (and subsequently h-index) within journal. The total amount of citations for the target group is bigger than 600. The control group is formed by another set of two editors from the same journal whose relations between their positions and their citation records remain neutral. The total amount of citations for the control group is more than 1200. The timespan for which citations' pattern has been studied is 1975-2015. Previous coercive citations for a journal benefit (increase its IF) has been signaled. To the best of our knowledge, this is a pioneering work on coercive citations for personal (editors') benefit. Editorial teams should be aware about this type of potentially unethical behavior and act accordingly.

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“…The Econophys data consists of a set of papers in the field of econophysics that were published between April 1995 and September 2010 in 78 scientific journals and an e-print server (i.e. arXiv.org) [21]. The network has 1608 distinct authors, 1204 papers and 19 061 links.…”

Section: Datamentioning

confidence: 99%

Identifying missing and spurious connections via the bi-directional diffusion on bipartite networks

2014

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Link prediction and spurious link detection in complex networks have attracted increasing attention from both physical and computer science communities, due to their wide applications in many real systems. Related previous works mainly focus on monopartite networks while these problems in bipartite networks are not yet systematically addressed. Containing two different kinds of nodes, bipartite networks are essentially different from monopartite networks, especially in node similarity calculation: the similarity between nodes of different kinds (called inter-similarity) is not well defined. In this letter, we employ the local diffusion processes to measure the inter-similarity in bipartite networks. We find that the inter-similarity is asymmetric if the diffusion is applied in different directions. Accordingly, we propose a bi-directional hybrid diffusion method which is shown to achieve higher accuracy than the existing diffusion methods in identifying missing and spurious links in bipartite networks.Link prediction and spurious link detection problems are longstanding challenges in network study [1]. They have applications in many fields such as chemistry, biology, sociology and computer science. In many biological networks, such as food webs, proteinprotein interaction networks and metabolic networks, whether a link between two nodes exists must be demonstrated by field or laboratorial experiments, which is usually very costly [2]. In addition, the data in constructing biological and social networks may contain inaccurate information, resulting in spurious links [3]. Correcting the network connections can be very expensive if it is done by laboratorial experiments. To solve this problem, many networkbased methods have been developed and they are shown to have high accuracy in identifying missing and spurious links [4].So far, most related works focus on monopartite networks in which only one type of nodes exists [5]. However, many systems coupled by two different building blocks should be modeled by bipartite networks. For example, the e-commercial systems consisting of online users and items [6], the scientific collaboration system consisting of authors and papers [7], family name inheritance system consisting of babies and names [8] are naturally described by such networks. The link prediction and spurious link detection problems are not yet well addressed in bipartite networks. One close problem is the so-called "network-based * Corresponding authors.

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Quantifying the influence of scientists and their publications: distinguishing between prestige and popularity

Cited by 91 publications

References 36 publications

Quantitative and Qualitative Analysis of Editor Behavior through Potentially Coercive Citations

Quantitative and Qualitative Analysis of Editor Behavior through Potentially Coercive Citations

Quantitative and Qualitative Analysis of Editor Behavior through maybe Coercive Citations

Identifying missing and spurious connections via the bi-directional diffusion on bipartite networks

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