Measuring the academic reputation through citation networks via PageRank

Massucci, Francesco Alessandro; Docampo, Domingo

doi:10.1016/j.joi.2018.12.001

Cited by 68 publications

(39 citation statements)

References 43 publications

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“…We compare the similarity of institution ranking between IPRank and IRank [11]. Both algorithms can be classified as structured metrics; however, the IPRrank is based on the heterogeneous institution-citation network whereas the IRank is based on the homogeneous citation network between institutions.…”

Section: Resultsmentioning

confidence: 99%

“…While many of these studies explore scientific impact at a particular timeframe, there's a growing interest in understanding the evolution of scientific impact in "science of science" [9], [10]. For scientific impact measurement, citation network is a often used technique [11], [12], whereas heterogeneous scholarly network has attracted growing attention recently [13], [14]. Quantifying scientific impact in the heterogeneous scholarly network is closely related to structural measure, citation analysis and behavioral complexity.…”

Section: Introductionmentioning

confidence: 99%

“…However, unstructured metrics rely heavily on the number of bibliometric indicators. To develop a structured quantitative method to measure the institutional impact, Massucci et al [11] integrated PageRank into the citation network of institutions. However, despite these significant efforts, the correlation between institutional impact and paper impact in heterogeneous scholarly network remains unclear.…”

Section: Introductionmentioning

confidence: 99%

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Measure the Impact of Institution and Paper Via Institution-Citation Network

Bai

Zhang

et al. 2020

IEEE Access

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This paper investigates the impact of institutes and papers over time based on the heterogeneous institution-citation network. A new model, IPRank, is introduced to measure the impact of institution and paper simultaneously. This model utilises the heterogeneous structural measure method to unveil the impact of institution and paper, reflecting the effects of citation, institution, and structural measure. To evaluate the performance, the model first constructs a heterogeneous institution-citation network based on the American Physical Society (APS) dataset. Subsequently, PageRank is used to quantify the impact of institution and paper. Finally, impacts of same institution are merged, and the ranking of institutions and papers is calculated. Experimental results show that the IPRank model better identifies universities that host Nobel Prize laureates, demonstrating that the proposed technique well reflects impactful research.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Measure the Impact of Institution and Paper Via Institution-Citation Network

Bai

Zhang

et al. 2020

IEEE Access

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“…Massucci (2019) use pagerank algorithms to study scientific articles in a campus. Researchers believe that increasing a rank scores can be done by increasing academic ranking scores, such as via conducting internal or external citations between campuses, and the geographical location of the campus [21]. However, internal citation is susceptible to self-plagiarism that can drop the campus rank.…”

Section: Pagerankmentioning

confidence: 99%

A Novel Approach to Rank Text-based Essays using Pagerank Method Towards Student’s Motivational Element

Arifin¹,

Che²,

Suryana³

2019

IJACSA

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Learning outcomes is one of the important factors to measure student achievement during the learning process. Today's learning is more focused on problem-solving and reasoning to existing problems than an ordinary problem. Most exams have been directed to analysis questions for students to think and synthesize. As such is troublesome for most students, they are not ready to answer the question, thus, their answers almost similar to their friends. This implies that the teacher has tried to guide students to work professionally and originally. However, the Teacher facing difficulty assessing student's work, especially if the assignments are conducted online without faceto-face instructions/discussions. To bridge such a gap, the teacher needs a method or algorithm to measure their rank to encourage students making an original answer. This research provides a solution in calculating students' ranks based on the similarity score of the essay answers. Pagerank is a ranking algorithm used by Google, this algorithm utilizes a Markov matrix that contains the direction of similarity score for each student. These scores are computed by the power method until converging. Rank is displayed to the teacher to review the similarity level of students' answers. As such is presented a line chart in which the x-axis refers to the students and the y-axis depicts the level of similarity. Ranking computation in Matlab produces an Eigen vector which acts as the rank measure. The higher the rank, the more similar is their answers to others. Hence, students with high ranks to work their answers more seriously ensure their original thoughts. In conclusion, the similarity score matrix using the PageRank algorithm can contribute to the teacher in providing peer motivation and encouraging student's internal motivation by presenting the ranked-answers presentation.

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“…Зарубежные и российские учёные озабочены не только разработкой эффективных инструментов её измерения [8], но и подходов к её формированию и повышению [9]. Так, этот показатель является наиболее весомым при определении позиции университета в QS World University Rankings -40%.…”

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University Academic Reputation as a Leadership Factor in the Global Educational Market

Antonova

Сущенко

2020

Vysš. obraz. Ross. (Print)

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The article discusses the university academic reputation as an indicator of its leadership in the global education services market. In the international rating systems for assessing the universities, academic reputation is one of the main indicators of status and prestige. Most higher education experts who look at the state of modern universities recognize that this factor is a key to attract the attention of the world academic community, industrial stakeholders, investors, prospective students and their parents to the university. We analyze the factors that improve university academic reputation using sources of empirical data such as QS World University Rankings database for 2017–2019 (open data analysis method), as well as the sociological research results including the opinions of experts (n = 5) (semi-structured interviews were selected as an appropriate method of data collection). The study results showed that China’s universities occupy a leading position. The key conclusion of the study is that the high academic reputation is one of the basic indicators of a university’s position in the global educational services market, but for leading universities it is a stigma reducing the need for innovation and the educational process development. BRICS Universities that create or change strategic development programme should be oriented towards achieving the leadership positions by not only institutional rankings but also subject and broad subject area rankings. This is a good indicator of the academic reputation growth for university, and it can be considered as a qualitative result of its goals and efficiency achieving in general.

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Measuring the academic reputation through citation networks via PageRank

Cited by 68 publications

References 43 publications

Measure the Impact of Institution and Paper Via Institution-Citation Network

Measure the Impact of Institution and Paper Via Institution-Citation Network

A Novel Approach to Rank Text-based Essays using Pagerank Method Towards Student’s Motivational Element

University Academic Reputation as a Leadership Factor in the Global Educational Market

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