Do PageRank-based author rankings outperform simple citation counts?

Fiala, Dalibor; Šubelj, Lovro; Žitnik, Slavko; Bajec, Marko

doi:10.1016/j.joi.2015.02.008

Cited by 30 publications

(9 citation statements)

References 31 publications

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“…In the co-citation analysis, some researchers questioned the efficiency of PageRank (Fiala et al, 2015), and some argued that simple indicators like frequency actually works better (Jiang, Sun, & Zhuge, 2013). All this evidence suggested researchers to choose the simple metric to identify the important themes.…”

Section: Discussionmentioning

confidence: 99%

Random walk on co‐word network: Ranking terms using structural features

Chiu

Lü

2015

Proc. Assoc. Info. Sci. Tech.

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This study proposes a weighted random walk method on co-word networks to identify important themes of a field using structural features of the networks. The goal is to test whether the weighted random walk method can be used to produce meaningful results on co-word networks. In addition, we examined the relationships among the results from the random walk method and other two common metrics for identifying important themes in a field: frequency and point centrality. Using a dataset of 17K bibliographic records for the articles in the LIS field from the Web of Science, our results indicate that all three measures are significantly correlated. A detailed comparison of the top terms ranked by the three metrics from the years of 2002-2006 and 2007-2012 is provided. The results show that the three measures are generally similar in revealing hotspots and development of the field. However, some noticeable differences are also found. The random walk method boosted the rankings of some lower ranked terms in the other two metrics (e.g. "universe", "servic" and "develop") due to their cooccurrences with top ranked terms (e.g. "information"). The findings of this study help to understand the use of random walk method on co-word networks. KeywordsCo-word analysis, random walk, co-word network.

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Section: Discussionmentioning

confidence: 99%

Random walk on co‐word network: Ranking terms using structural features

Chiu

Lü

2015

Proc. Assoc. Info. Sci. Tech.

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“…They are: Web of Science as Eigenfactor Score [18]- [20] and Scopus as SJR indicator [45], [46]. Considering the distinct importance of different entities and relationships in academic networks, researchers have proposed a number of weighting schemes, together with the variants of PageRank or HITS algorithm, to evaluate the scientific impact of articles [42], [85]- [88], journals [24], and scientists [89]- [91]. In addition, researchers also combine the citation and network-based evaluation metrics to measure the impact of scholars.…”

Section: B Network-based Methodsmentioning

confidence: 99%

Quantifying a Paper’s Academic Impact by Distinguishing the Unequal Intensities and Contributions of Citations

Wang

Ren

et al. 2019

IEEE Access

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Citation count is a widely-used indicator for calculating the academic impact of scientific papers, but it is limited because it assumes all citations are of similar value and weights each equally. By examining the influence changes in papers' citation distribution and the cited papers' unequal contributions to the citing ones, this study aims to distinguish citations and, on this basis, evaluate the academic impact of the papers. Three indices of time-weighted citation count, citation width and citation depth are proposed to distinguish citations and perform the evaluation task. The experimental results show that papers exhibit different influence intensity characteristics in different periods of citation life. Those papers got larger citations in recent years are more influential and more active to gain new citations. The papers show the different scope of influence in their citing environment, although they were originally published in the same journal and the same year. In addition, the different frequency of mentions and the different subject similarities with the citing works suggest that the papers have different importance and usefulness for subsequent research. These results suggest that these three indices do help to distinguish citations and reveal the different intensity and contribution of influences in citations. Finally, the three indices are integrated into the overall evaluation of the academic impact of the paper, and the weight of each index is calculated by the entropy weight method. Quite different overall impacts in the paper are shown due to their different performances in the three indices, even though they have the same total number of citations. INDEX TERMS Academic impact, time window, knowledge flow, mentioned frequency, entropy weight method.

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“…Based them. There also exist some works that utilize the PageRank algorithm and the HITS algorithm in the meantime to find the more appropriate one [32]. Instead of applying single kind of algorithm, researchers also combine the PageRank and HITS algorithm together to measure the scientific impact in order to utilize the advantages of both algorithms.…”

Section: Network-based Methodsmentioning

confidence: 99%

AIRank: Author Impact Ranking through Positions in Collaboration Networks

Zhang

Ning

et al. 2018

Complexity

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Citation is a universally acknowledged way for scientific impact evaluation. However, due to its easy manipulability, simply relying on citation cannot objectively reflect the actual impact of scholars. Instead of citation, we utilize the academic networks, in virtue of their available and abundant academic information, to evaluate the scientific impact of scholars in this paper. Through the collaboration among scholars in academic networks, we notice an interesting phenomenon that scholars in some special positions can access more kinds of information and connect researchers from different groups to promote the scientific collaborations. However, this important fact is generally ignored by the existing approaches. Motivated by the observations above, we propose the novel method AIRank to evaluate the scientific impact of scholars. Our method not only considers the impact of scholars through the mutual reinforcement process in heterogeneous academic networks, but also integrates the structural holes theory and information entropy theory to depict the benefit that scholars obtain via their positions in the network. The experimental results demonstrate the effectiveness of AIRank in evaluating the impact of scholars more comprehensively and finding more top ranking scholars with interdisciplinary nature.

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Do PageRank-based author rankings outperform simple citation counts?

Cited by 30 publications

References 31 publications

Random walk on co‐word network: Ranking terms using structural features

Random walk on co‐word network: Ranking terms using structural features

Quantifying a Paper’s Academic Impact by Distinguishing the Unequal Intensities and Contributions of Citations

AIRank: Author Impact Ranking through Positions in Collaboration Networks

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