Would it be possible to increase the Hirsch-index, π-index or CDS-index by increasing the number of publications or citations only by unity?

Vinkler, Péter

doi:10.1016/j.joi.2012.08.001

Cited by 10 publications

(8 citation statements)

References 27 publications

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“…where we set the cbias as 0 if the self-citations are less than 5% of the citations for respective paper and cbias is set to the biased value exceeding the 5% limit otherwise. From Equations (2,4,6), net-citations (denoted by ncit) can simply be calculated as, ncit = gcit − f j (jbias) + f a (abias) + f c (cbias) .…”

Section: Co-author Biasmentioning

confidence: 99%

“…In this work, the author has evaluated 20 variants on the dataset of mature scientists and found that ranking the publications based on their number of citations is not a very useful tool. In [4], it is observed that the score of various indices under study increases the number of publications. In [5], 108 variants have been reviewed to find out correlation among those articles.…”

Section: Introductionmentioning

confidence: 99%

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Self-Citation Analysis on Google Scholar Dataset for H-Index Corrections

et al. 2019

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For the recent decades, self-citations have been extensively studied by the academia, therein Web of Science (WoS) citations count and h-index are being considered as the benchmark parameters. The WoS is used to determine citations based on the Institute for Scientific Information's (ISI) master list. Towards this end, Google Scholar maintains a broad source of the research articles. However, Google Scholar does not exclude self-citations from the list of citations of one particular journal, author or co-author. The Google Scholar citation statistics are, therefore, not regarded as highly accurate. In this paper, we propose an updated h-index for Google Scholar by first quantifying and thereafter excluding the self-citations from the h-index. We target the two aspects of Google Scholar that belong to the evaluation of the quality of Google Scholar sources and the self-citation' records available in the citation lists. In our analysis, we have taken two datasets. The first dataset is composed of scientists awarded by the Scientometrics, which is recorded from the Google Scholar. According to this dataset, 28 scientists have been awarded as best researchers on the basis of their maximum citations and the h-indexes. The second dataset includes 16 non-award winner scientists. Both datasets include records falling in the period of 1984 to 2017. Based on analysis of award winner scientists' data, the aggregated journal self-citations are observed as 3.95%, whereas author and coauthor self-citations are found as 2.86% and 3.33%, respectively. In contrast, non-award winner scientists have average journal biased citation as 1.22%, author biased citations as 0.41% and co-author biased citations as 0.90%. We consider three types of the self-citations, i.e., journal, author, and coauthor for each scientist to cumulatively calculate the revised h-index. We obtained a new ranking of the scientists, which is based on a more accurate updated h-index. The updated h-index for the Google Scholar can be used for a more accurate academic ranking of the authors and the research articles.

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Section: Co-author Biasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Self-Citation Analysis on Google Scholar Dataset for H-Index Corrections

et al. 2019

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“…VM konusunda bildiri makalelerine en fazla yer veren (Thomson and Reuters tarafından taranan-Conference Proceedings Citation Index) konferanslar şunlardır: Bilindiği üzere internet teknolojisindeki gelişmelere paralel olarak; bilimsel dergiler de çevrim içi makale başvuru ve hakemlik/değerlendirme sistemleriyle süreçleri kolaylaştırma çabasına girmişlerdir. Benzer şekilde; araştırmacılar da kaynaklara daha kolay erişebilir bir hale gelmiş ve makale başvuru sayılarında/yayınlanan makale sayılarında ciddi artışlar yaşanmıştır (Vinkler, 2013). Bu nedenle; bir bilimsel alandaki gelişmeleri düzenli olarak takip edebilmek oldukça zorlu bir hal almıştır.…”

Section: şEkil 1 Vm Makalelerin Yıllar Içindeki Dağılımıunclassified

Veri̇ Madenci̇li̇ği̇ Çalişmalari Üzeri̇ne Bi̇r Anali̇z: Türki̇ye Adresli̇ Yayinlar

Durmuşoğlu¹

2017

Elektronik Sosyal Bilimler Dergisi

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Alptekin DURMUŞOĞLU 1 ÖzVeri Madenciliği (VM); ham ve yığınlar halinde olan veri setlerinde, doğrudan tespit edilemeyen, gizli ve anlamlı bilgilerin keşfedilmesine yönelik olarak bir dizi yöntemin kullanıldığı bir araştırma alanıdır. Son yıllarda, veri depolama ve veri toplama teknolojilerindeki ucuzlamaya paralel olarak artan veri miktarıyla VM uygulamaları tüm dünyada giderek yaygınlaşmıştır. Uygulamadaki sayıca artış, konuyla alakalı bilimsel yazındaki makalelerin de sayıca artışını beraberinde getirmiştir. Bahsi geçen yayınların, bilimsel alandaki genel gidişatını belirlemek, gerek araştırmacılar gerekse uygulayıcılar için çok çeşitli faydalar sağlayacaktır. Bu doğrultuda; bu çalışmada, son on yılda indeksli dergilerde (Science Citation Index-SCI, Science Citation Indexexpanded, Social Sciences Citation Index-SSCI ve Arts and Humanities Citation Index-AHCI) ve konferanslarda yayınlanan (Conference Proceedings Citation Index) VM çalışmaları ele alınmakta, bu çalışmaların; ülke, yazar, yöntem vb. dağılımları ortaya konmaktadır. Ayrıca Türkiye adresli VM çalışmaları da ayrı bir başlık altında ele alınmış ve bu çalışmaların nitelik ve nicelikleri irdelenmiştir. Anahtar kelimeler: Veri Madenciliği, bibliyometrik analiz, Türk araştırmacılar AbstractData Mining (DM) is a research field where a sequence of methods are applied to discover hidden and significant knowledge that cannot be directly detected inside bulk and raw data sets. In the recent years with the increasing amount of data in parallel to the advances in data storage and collection technologies, DM applications have become widespread. Increase in the number of applications has entailed the increase in the number of scientific publications. Determining the general state of affairs can provide several benefits both for the implementers and the researchers. In this direction, the DM studies published in indexed journals (Science Citation Index-SCI, Science Citation Index-expanded, Social Sciences Citation Index-SSCI ve Arts and Humanities Citation Index-AHCI) and conferences (Conference Proceedings Citation Index) on the last ten years has been covered and the country, author , method etc. distribution of those studies has been put forward. In addition to that, the DM studies addressing Turkey has been handled under a separate title and qualifications and quantifications have been examined.

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“…A different way of manipulating the H-index is by strategic self-citations [10,28]; Bartneck and Kokkelmans [3] consider approaches to detect these. Strategic self-citations take some effort and are irreversible.…”

Section: Introductionmentioning

confidence: 99%

H-index manipulation by merging articles: Models, theory, and experiments

Bevern¹,

Komusiewicz²,

Niedermeier³

et al. 2016

Artificial Intelligence

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An author's profile on Google Scholar consists of indexed articles and associated data, such as the number of citations and the H-index. The author is allowed to merge articles; this may affect the H-index. We analyze the (parameterized) computational complexity of maximizing the H-index using article merges. Herein, to model realistic manipulation scenarios, we define a compatibility graph whose edges correspond to plausible merges. Moreover, we consider several different measures for computing the citation count of a merged article. For the measure used by Google Scholar, we give an algorithm that maximizes the H-index in linear time if the compatibility graph has constant-size connected components. In contrast, if we allow to merge arbitrary articles (that is, for compatibility graphs that are cliques), then already increasing the H-index by one is NP-hard. Experiments on Google Scholar profiles of AI researchers show that the H-index can be manipulated substantially only if one merges articles with highly dissimilar titles.

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Would it be possible to increase the Hirsch-index, π-index or CDS-index by increasing the number of publications or citations only by unity?

Cited by 10 publications

References 27 publications

Self-Citation Analysis on Google Scholar Dataset for H-Index Corrections

Self-Citation Analysis on Google Scholar Dataset for H-Index Corrections

Veri̇ Madenci̇li̇ği̇ Çalişmalari Üzeri̇ne Bi̇r Anali̇z: Türki̇ye Adresli̇ Yayinlar

H-index manipulation by merging articles: Models, theory, and experiments

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