Author ranking evaluation at scale

Dunaiski, Marcel; Geldenhuys, Jaco; Visser, Willem

doi:10.1016/j.joi.2018.06.004

Cited by 24 publications

(11 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, by comparing 17 network-based ranking algorithms, a recent study [41] found that time-rescaled versions of PageRank and its variant LeaderRank [11] are the best-performing algorithms in the identification of expert-selected seminal papers and patents. PageRank is also effective in identifying influential researchers [53]. However, PageRank performs poorly in other problems.…”

Section: Performance Variabilitymentioning

confidence: 99%

Network-based ranking in social systems: three challenges

Lü¹

2020

J. Phys. Complex.

View full text Add to dashboard Cite

Ranking algorithms are pervasive in our increasingly digitized societies, with important real-world applications including recommender systems, search engines, and influencer marketing practices. From a network science perspective, network-based ranking algorithms solve fundamental problems related to the identification of vital nodes for the stability and dynamics of a complex system. Despite the ubiquitous and successful applications of these algorithms, we argue that our understanding of their performance and their applications to real-world problems face three fundamental challenges: (1) rankings might be biased by various factors; (2) their effectiveness might be limited to specific problems; and (3) agents' decisions driven by rankings might result in potentially vicious feedback mechanisms and unhealthy systemic consequences. Methods rooted in network science and agent-based modeling can help us to understand and overcome these challenges.

show abstract

Section: Performance Variabilitymentioning

confidence: 99%

Network-based ranking in social systems: three challenges

Lü¹

2020

J. Phys. Complex.

View full text Add to dashboard Cite

show abstract

“…Recent researches have been inspired from the PageRank algorithm and have used the structural features of scholarly networks to assess the author impact [32]- [35]. Also, social network measures such as degree centrality, closeness centrality, betweenness centrality, and PageRank frequently are used to assess author impact [14], [19], [32], [34], [36]- [40]. In addition, Tweets are used to quantify author impact [41].…”

Section: Feature Selectionmentioning

confidence: 99%

“…Although researchers have delivered various achievement VOLUME 4, 2016 in author impact evaluation and prediction, many challenging problems remains unresolved [19]- [23]. The heterogeneous attribute and the dynamic nature of big scholarly data lead to highly diversified scholarly networks, which raises the challenge in exploring the relationship between authors and other scholarly entities.…”

Section: Introductionmentioning

confidence: 99%

Author Impact: Evaluations, Predictions, and Challenges

2019

View full text Add to dashboard Cite

Author impact evaluation and prediction play a key role in determining rewards, funding, and promotion. In this paper, we first introduce the background of author impact evaluation and prediction. Then, we review recent developments of author impact evaluation, including data collection, data pre-processing, data analysis, feature selection, algorithm design, and algorithm evaluation. Thirdly, we provide an in-depth literature review on author impact predictive models and common evaluation metrics. Finally, we look into the representative research issues, including author impact inflation, unified evaluation standards, academic success gene, identification of the origins of hot streaks, and higher-order academic networks analysis. This paper should help the researchers obtain a broader understanding in author impact evaluation and prediction, and provides future research directions.

show abstract

“…Different approaches have been used in literature to analyze the author's ranking. Authors have also shown the use of page rank algorithm on the author co-citations network to get the respective ranking (Ding, Yan, Frazho and Caverlee, 2009;Nykl, Campr and Ježek, 2015;Dunaiski, Visser and Geldenhuys, 2016;Dunaiski, Geldenhuys and Visser, 2018). Usman et al have shown in their research the analysis of various assessment parameters like ℎ-index, citations, publications, authors per paper, -index, hg-index (Alonso, Cabrerizo, Herrera-Viedma and Herrera, 2010), R-index (Jin, Liang, Rousseau and Egghe, 2007), e-index (Zhang, 2009a), h'-index (Zhang, 2013), w-index (Zhang, 2009b) etc.…”

Section: Introductionmentioning

confidence: 99%

Impact of $h$-index on authors ranking: An improvement to the h-index for lower-ranked author

Khurana,

Sharma

2021

Preprint

View full text Add to dashboard Cite

To check the impact of ℎ-index on authors ranking within a university based on Scopus and WoS.• The introduction of ℎ which is a complement to ℎ-index, takes into account the weight of the highest cited paper.• Giving weight to highest cited paper significantly improves the performance of the lower-ranked authors, especially for ℎ ≤ 10. For higher rank, ℎ == ℎ and for lower rank, ℎ ≥ ℎ.• The performance of Scopus and WoS varies among disciplines, hence the ranking varies.

show abstract

Author ranking evaluation at scale

Cited by 24 publications

References 31 publications

Network-based ranking in social systems: three challenges

Network-based ranking in social systems: three challenges

Author Impact: Evaluations, Predictions, and Challenges

Impact of $h$-index on authors ranking: An improvement to the h-index for lower-ranked author

Contact Info

Product

Resources

About