Finding Rising Stars in Social Networks

Daud, Ali; Abbasi, Rashid; Muhammad, Faqir

doi:10.1007/978-3-642-37487-6_4

Cited by 47 publications

(26 citation statements)

References 15 publications

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“…However, PubRank only mentioned static ranking of publication, authors and papers mutual influence. Later, StarRank enhanced PubRank in two aspects, considering co-authors' mutual influence, and using dynamic ranking lists of publications λ(d pq) instead of static ranking [75]. It also considered the first author as the major contributor, the AOWI is author order weight based mutual influence, which is defined as the proportion of authors' co-authored contributions to the total contribution of author A j .…”

Section: Identifying Rising Starsmentioning

confidence: 99%

Prediction methods and applications in the science of science: A survey

Hou

Pan

Guo

et al. 2019

Computer Science Review

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Science of science has become a popular topic that attracts great attentions from the research community. The development of data analytics technologies and the readily available scholarly data enable the exploration of data-driven prediction, which plays a pivotal role in finding the trend of scientific impact. In this paper, we analyse methods and applications in data-driven prediction in the science of science, and discuss their significance. First, we introduce the background and review the current state of the science of science. Second, we review data-driven prediction based on paper citation count, and investigate research issues in this area. Then, we discuss methods to predict scholar impact, and we analyse different approaches to promote the scholarly collaboration in the collaboration network. This paper also discusses open issues and existing challenges, and suggests potential research directions.

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Section: Identifying Rising Starsmentioning

confidence: 99%

Prediction methods and applications in the science of science: A survey

Hou

Pan

Guo

et al. 2019

Computer Science Review

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“…This venue score is defined as entropy of venue (high rank venues have less entropy and the low rank have high) (Daud et al 2013). It is formulated as…”

Section: Venue Specificity Score (Vss)mentioning

confidence: 99%

“…By adding the authors order as they appear in the papers, the corresponding authors are considered as maximum contributors (Daud et al 2013). The author contribution weight is calculated as…”

Section: Author Contribution (Ac)mentioning

confidence: 99%

“…It categorized the authors into different groups using unsupervised learning techniques. The PubRank algorithm was upgraded on StarRank (Daud et al 2013) by embedding author's contribution based mutual influence and dynamic ranking lists of publications as new features. However there is no work done for predicting rising star using classification techniques.…”

Section: Introductionmentioning

confidence: 99%

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Using machine learning techniques for rising star prediction in co-author network

et al. 2014

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Online bibliographic databases are powerful resources for research in data mining and social network analysis especially co-author networks. Predicting future rising stars is to find brilliant scholars/researchers in co-author networks. In this paper, we propose a solution for rising star prediction by applying machine learning techniques. For classification task, discriminative and generative modeling techniques are considered and two algorithms are chosen for each category. The author, co-authorship and venue based information are incorporated, resulting in eleven features with their mathematical formulations. Extensive experiments are performed to analyze the impact of individual feature, category wise and their combination w.r.t classification accuracy. Then, two ranking lists for top 30 scholars are presented from predicted rising stars. In addition, this concept is demonstrated for prediction of rising stars in database domain. Data from DBLP and Arnetminer databases (1996-2000 for wide disciplines) are used for algorithms' experimental analysis.

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“…The self-citations should not be given the same weight as citations by others issue is investigated and F-Index [13] was proposed. Finding the rising star in academia [14] where the star is the authors who have not enough citation at the start of their career but predicted as a rising star in the future. Their contribution actually highlights the new researchers irrespective of traditional indexing methods to give credits to researchers on the basis of citations and number of publications.…”

Section: Introductionmentioning

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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Finding Rising Stars in Social Networks

Cited by 47 publications

References 15 publications

Prediction methods and applications in the science of science: A survey

Prediction methods and applications in the science of science: A survey

Using machine learning techniques for rising star prediction in co-author network

Global Citation Impact rather than Citation Count

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