Measuring the academic reputation through citation networks via PageRank

The objective assessment of the prestige of an academic institution is a difficult and hotly debated task. In the last few years, different types of University Rankings have been proposed to quantify the excellence of different research institutions in the world. Albeit met with criticism in some cases, the relevance of university rankings is being increasingly acknowledged: indeed, rankings are having a major impact on the design of research policies, both at the institutional and governmental level. Yet, the debate on what rankings are exactly measuring is enduring. Here, we address the issue by measuring a quantitive and reliable proxy of the academic reputation of a given institution and by evaluating its correlation with different university rankings. Specifically, we study citation patterns among universities in five different Web of Science Subject Categories and use the algorithm on the five resulting citation networks. The rationale behind our work is that scientific citations are driven by the reputation of the reference so that the PageRank algorithm is expected to yield a rank which reflects the reputation of an academic institution in a specific field. Our results allow to quantifying the prestige of a set of institutions in a certain research field based only on hard bibliometric data. Given the volume of the data analysed, our findings are statistically robust and less prone to bias, at odds with ad--hoc surveys often employed by ranking bodies in order to attain similar results. Because our findings are found to correlate extremely well with the ARWU Subject rankings, the approach we propose in our paper may open the door to new, Academic Ranking methodologies that go beyond current methods by reconciling the qualitative evaluation of Academic Prestige with its quantitative measurements via publication impact.


page 8

page 9

page 13

page 15


A sensitivity analysis of research institutions' productivity rankings to the time of citation observation

One of the critical issues in bibliometric research assessments is the t...

Mining university rankings: Publication output and citation impact as their basis

World University rankings have become well-established tools that studen...

Approximating percentage of academic traffic in the World Wide Web and rankings of countries based on academic traffic

The paper introduces a novel mechanism for approximating traffic of the ...

The Role of Positive and Negative Citations in Scientific Evaluation

Quantifying the impact of scientific papers objectively is crucial for r...

Rigging Research Results by Manipulating Top Websites Rankings

Researchers often use rankings of popular websites when measuring securi...

Feature Engineering and Ensemble Modeling for Paper Acceptance Rank Prediction

Measuring research impact and ranking academic achievement are important...

Impact of Geographic Diversity on Citation of Collaborative Research

Diversity in human capital is widely seen as critical to creating holist...

I Introduction

Academic institutions share fundamental missions related to the education and socialisation of students and the advancement of knowledge. Besides, there is an increasing demand on universities to make their knowledge available to society and establishing links with the socio-economic context in which they carry out their activities. Any of these three missions, namely education, research, and social engagement, can therefore be the source of institutional reputation.

The fact of the matter is that the research mission arguably represents the most visible part of any academic outfit, and as such it has become the main source of institutional reputation. The main reason behind this is the adoption of global standards rooted on the consensus generated by peer review in the evaluation of scientific advances. Besides, research outcomes are easier to measure: there is nowadays a plethora of instruments to analyze the scientific production of individual researchers, institutions and countries. Bibliometric data, i.e. counts of papers and citations, have been the fodder of all these instruments. Compacts of bibliometric information are periodically released by academic units and specialist consultancies (e.g. Liu and Cheng, 2005; Waltman et al, 2012).

Because of their relatively easier quantification, bibliometric indicators of research performance (usually related to individual and institutional research outcomes) feed a number of academic rankings (Aguillo et al, 2010). Although it is not entirely clear to which degree the results of those rankings constitute a trustworthy proxy of the academic reputation of a given institution (Jeremic et al, 2011), and despite scholarly agreement on the lack of appropriateness of ranking methodologies,“rankings are now widely perceived and used as the international measure of quality” (Hazelkorn, 2018). Moreover, since rankings “define what –world-class–is to the broadest audience, they cannot be ignored by anyone interested in measuring the performance of tertiary education institution” (Salmi, 2009).

In particular, despite its numerous weaknesses, acknowledged willingly by its authors, the Shanghai academic ranking of World Universities (ARWU) has triggered reform initiatives aimed at fostering excellence and recognition, illustrating again the potency of bench-marking (Aghion et al, 2008). The results from the Shanghai ranking have been used to assess the research strengths and shortcomings of national higher education systems (Docampo, 2011), and have been shown to be reliably connected with the research excellence of a given institution (Dehon et al, 2010; Docampo and Cram, 2014).

Our contribution is intended in the path traced by recent research in bibliometrics: just like journals evaluation cannot be captured in just one metric (Moed et al, 2012), analysing the research performance of academic institutions is also a very complex task, thus reducing it to a single measure may not be the way forward. Therefore, our investigation should be interpreted as a source of sound information to be used within or in combination with academic classification results for bench-marking purposes. In a thorough critical overview of the value and limits of world university rankings, Moed (2017) points to a lack of consistency in the way several well established international classifications identify scientific excellence and calls for carefully combining information from different ranking systems to get a more comprehensive view on what indicators measure. Moreover, in a recent systematic review of the usefulness of university rankings to improve research, Vernon et al (2018) assert the need for new measures that emphasize quality over quantity to affirm research performance improvement initiatives and outcomes. They also suggest that future research should help in evaluating three dimensions of research outcomes: scientific impact, economic outcomes, and public health impact. In line with this reflection, our contribution aims at providing new measurements that emphasise quality over quantity in relation to scientific impact and relevance. However, we would like to warn the reader again on the inherent complexity of such an endeavor, and that the results we produce should be read in combination with the results of current ranking exercises.

We begin our discussion by acknowledging that the legitimate question of how academic rankings are effectively related with the ‘intrinsic’ quality of a given university remains partly unanswered: indeed, one may argue that reputation is built upon the perception of excellence, which may somehow transcend mere bibliometric data. To achieve their scoring results, some rankings (e.g. ARWU) are mainly shaped by publication and staff figures, while other classifications (e.g. THE and QS) although making use of a number of bibliometric indicators rely heavily upon surveys aimed at capturing reputation. However, those surveys may be prone to biases, due to the size of the surveyed cohort, to the distribution of it across different scientific disciplines and, ultimately, to human error due to possible confusion among affiliation names.

Yet, researchers have a very concrete and measurable way to credit reputation, that is, via citations to their peers’ work. Indeed, it is very reasonable to assume that, if researcher cites a work by researcher in one of her publications, she deems that work (and thus its author) a reputable source of information. By means of this mechanism, it is also fair to assume that if a researcher receives many citations, her academic reputation is globally recognised. Finally, one may also assume that if some researcher is cited by one of her prestigious peers, her reputation is also increased.

This ‘reputation attribution’ mechanism is well captured by a well-known algorithm, i.e. the PageRank algorithm, initially developed by Pinski and Narin (1976), and later adapted by Brin and Page (1998) to rank web pages according to their importance on the web. In a nutshell, given a network of entities citing one another, the PageRank algorithm assigns a score to each entity which is based both on the number of citations the entity receives and on the reputation of the citing institutions. In essence, entities in the network have a high PageRank either if many other entities cite it, or if a few other entities with a high PageRank cite it. By aggregating the above scheme at the level of institutions, one should be able to discern which institutions are deemed as more reputable by their peers, by looking at how different universities cite each other.

In this paper, we specifically explore the issue of quantifying the academic reputation of a certain academic institution and to relate this measurement to the score the institution attains in a given university ranking. We do so based solely on hard bibliometric data and by exploiting the PageRank algorithm. To achieve so, we use Web of Science records on publications and citations provided by Clarivate Analytics.

Albeit one might object that, by analysing citation patterns, we are in fact measuring impact, it is fair to say that PageRank applications are generally framed within the context of measuring prestige, rather than impact (see, for instance, Radicchi, 2011). As we show in Sec. IV, rankings based on citation counts yield in fact different results from PageRank, showcasing how PageRank is able to capture dynamics that go somehow beyond ‘classical’ bibliometric definitions of impact111Besides, academic rankings, while aimed at measuring Academic Prestige, measure, in practice, impact in some way or another – by counting, for instance, papers in Nature and Science, and highly cited scientists. Hence, one can fairly say that, in the academic context at least, impact and prestige are two tightly related concepts.. Also, although the suitability of the linearity of equations that lie at the core of the PageRank algorithm have been questioned by some researchers to be correctly capturing the non-linear dynamics of scientific collaboration and subsequent perceived prestige (see Lu et al (2009) and Ghasemian et al (2018)), we show in our work that the application of PageRank on whole academic institutions, in specific academic fields, yields very reasonable results that offer a nice compromise between academic rankings based on bibliometric data (such as ARWU GRAS) and those largely based on reputational surveys (such as the QS Subject Rankings).

Ii Relation to prior work

The idea of university rankings that are based exclusively on bibliometric statistics is not new. Two well-known global classifications, the Leiden ranking (CWTS, 2017), and the National Taiwan university ranking (NTU, 2017) rely solely on bibliometric data. Besides, prestige-based procedures to assess scientific impact have been flourishing since PageRank was introduced in the realm of academic evaluation (Zhang, 2016): PageRank has indeed been applied to rank scientific journals [in this context, see for instance Yates and Dixon (2015); Foulley et al (2017), or consider the well-known cases of the Article Influence Score (Rizkallah and Sin, 2010), the EigenFactor (Bergstrom, 2007) or the Scimago Journal Ranking (SJR, 2007)], or to rank individual researchers (Gao et al, 2016; Senanayake et al, 2015; Ding et al, 2009; Radicchi et al, 2009). The rationale behind the use of PageRank has been well elucidated by Luo et al (2018), by showing how prestigious citations can be “effectively and efficiently identified through the source affiliations of the citing paper”. Nykl et al (2014) ranked authors of scientific publications based on citation analyses, through the examination of networks of publications, authors and journals. Their results stand in support of the use of PageRank based procedures, rather than non-iterative approaches. Also Dunaiski et al (2016) evaluated different algorithms that can be applied to bibliographic citation networks to rank scientific papers and authors. While their results recognise the relevance of citations to measure high-impact, their findings also indicate that PageRank based algorithms are better suited to rank important papers or to identify high-impact authors. Kazi et al (2016)

propose to overcome the reliance of impact methods on pure quantitative measures by using context based on three specific quality factors, namely sentiment analysis of the text surrounding the citation, self-citations, and semantic similarity between citing and cited article. Their experimental results seem to improve traditional citation counts and are similar to those rendered by PageRank based methods.

Surprisingly enough, however, PageRank has been scantly applied at the level of academic institutions, where the noise due to erroneous/missing publication attributions is certainly much smaller than for the case of single researchers. Here, we make the educated guess that universities aggregate citations in the same way as single researchers do, so that institutions with high PageRank have a higher reputation in the network of academic institutions in a given research field.

To the best of our knowledge, the first attempt to use PageRank  based procedures to evaluate institutions was accomplished by Lages et al (2016) with the introduction of the Wikipedia ranking of world universities: this ranking was based on the PageRank results applied to the directed networks between articles of 24 Wikipedia language editions. The Wikipedia ranking relies on a statistical evaluation of world universities which, according to their creators “can be viewed as a new independent ranking being complementary to already existing approaches”. Lages et al (2016) compared their PageRank list of top 100 universities with the ARWU-500 list and found a 62% overlapping, indicating that their analysis gives reliable results.

But Wikipedia citation patterns may escape the dynamics that actually shape reputation in the Academic world. Therefore, we aim in this work at applying PageRank readily to the network of citations that institutions build by citing each others’ scientific publications. The dynamics that generate those networks should resemble more closely those that are behind the construction of academic prestige (or the perception thereof). Much in the same way as Lages et al (2016) used a well established global ranking to test the reliability of their results, we will also try to compare our results with well established evaluation efforts to check the validity of our approach. Clearly, citation patterns and reputation depend closely on the academic field. For this reason, we carry out our analysis on five distinct Web of Science categories which, incidentally, map one–to–one onto five scientific fields covered by the ARWU thematic Global Rankings of Academic Subjects (ARWU-GRAS). Our work helps to shed some light on how the academic prestige of ranked institutions is captured by the GRAS rankings and helps reconciling ‘qualitative’ and ‘quantitative’ ranking approaches by providing a method that combines publication metrics and reputation in a quantitative fashion.

Iii Materials and methods

iii.1 A brief account of the ARWU Global Ranking of Academic Subjects

Launched in 2017, the Global Ranking of Academic Subjects (ARWU-GRAS) ranks institutions, presenting a minimum number of research articles in a five year period, in 52 subjects across natural sciences, engineering, life sciences, medical sciences, and social sciences. Four bibliometric indicators (related to the scientific production from 2011 to 2015 for the 2017 edition of the ranking) are present in all the subjects. For each institution and academic subject, those indicators are:

  • number of papers “article” type) authored by an institution.

  • Category Normalized Citation Impact of the records used to compute indicator PUB.

  • Percentage of articles with at least two different countries in the list of addresses.

  • Number of papers published in Top Journals.

Besides, a fifth indicator, AWARD, related to winners of specific awards applies to 30% of the ARWU-GRAS academic subjects. Different publication thresholds and sets of indicator weights are used depending on the academic subject. The Ranking Methodology webpage222 describes the indicators in more detail. For each indicator, ARWU-GRAS scores are calculated following a procedure that can be summarized as follows: first multiply each value of the gathered raw data by a fixed scaling factor so that the largest raw value is scaled to 10000. Then, compress the dynamic range of the scaled raw data by taking its square root to form the indicator score (Docampo, 2013; Docampo and Cram, 2014). Finally, using the weights allocated by ARWU-GRAS to each of the ranked subjects, compute the weighted sum of the indicator scores.

The weights used by ARWU-GRAS for the indicators in the five academic subjects are listed in Table 1.

Research Subject PUB CNCI IC TOP AWD
Dentistry & Oral Sciences (DEN) 100 100 20 100 100
Finance (FIN) 150 50 10 100 0
Library & Information Science (LIB) 150 50 10 100 0
Telecommunication Engineering (TEL) 100 100 20 100 0
Veterinary Sciences (VET) 100 100 20 200 0
Table 1: Weights allocated to ARWU-GRAS indicators in the five subjects under analysis

iii.2 The data analysed

Five Web of Science categories have been analysed in this paper: Dentistry, Oral Surgery & Medicine; Business, Finance; Information Science & Library Science; Telecommunications; Veterinary Sciences. The choice of those five Categories is not accidental: indeed, they correspond to ARWU-GRAS equivalents as listed in Table 1 (ARWU, 2017). Moreover, three of the chosen WoS categories (Dentistry, Oral Surgery & Medicine, Business, Finance, and Veterinary Sciences) can also be qualitatively mapped to three corresponding QS thematic rankings. QS thematic rankings are produced annually to identify top universities in a specific subjects. To do so, QS uses citations as well as global surveys of employers and academics.

The choice of scientific subjects, therefore, enables us to make meaningful comparisons between our method and well established academic rankings. To that effect, we have downloaded the overall scores in the QS ranking of the institutions shown in their official webpage –50 institutions in Veterinary Science and Dentistry, and 200 in Accounting and Finance. Since we can reproduce the results of ARWU-GRAS using direct data from the bibliometric suite InCites, we have made use of the official scores for institutions that are listed on the ARWU-GRAS website, and have extended the subject rankings to include all the institutions over the threshold of the minimum number of publications set by the ARWU-GRAS methodology. The list of institutions (ARWU-GRAS webpage) comprised 200 universities in all the subjects considered here, but Telecommunication Engineering (300) and Library & Information Science (100). The total number of institutions analysed in the paper is shown in Table 2

. We analyse bibliometric records classified as ‘article-type’ in the Clarivate’s Web of Science database, in any of the research categories corresponding with the list of five subjects included in Table

1. Data from the InCites platform containing all articles published in the time window 2010–2014 (both included) were provided by Clarivate Analytics in raw markup language files. For each publication, we retained the affiliation of all authors and of all references, respectively.

The data comprise 188,533 unique bibliographic records ascribed to 5,063 unique affiliations and citing 2,907,556 indexed references. In order to compare results with the ARWU-GRAS rankings, for each WoS category only records pertaining to affiliations showing a total number of articles in excess of the publication threshold of the corresponding ARWU-GRAS subject ranking were retained. In turn, only cross-citations among those publications were considered to build the networks analysed and to compute the corresponding PageRank score. Once this specific subset of affiliations, publications, and citations was retained for each WoS category, we ended up with five different networks whose characteristics are reported in the next section.

iii.3 Network properties and metrics

In Network Theory, a weighted, directed network is composed of a set of nodes and a (possibly) non–symmetric matrix of weights . For each it exists a weighted and directed link between nodes and of the network, with an associated weight . The adjacency matrix of the network is given by , where is the Kronecker delta: is one whenever there is a (directed) link connecting node to node and zero otherwise.

Based on the definition above, the in–degree of a node is given by


while the in–degree distribution is given by


Finally, the degree centrality of a node is equal to


and it is upper bounded by 1 (when excluding self-links).

Since the networks we analyse differ in size, their node can attain a different maximum degree. For this reason, to make meaningful comparison among the different networks, in Fig. 1 we plot the degree centrality distribution, defined as:


and refer to degree centrality when mentioning ‘central’ nodes in Sec. IV.

iii.4 The PageRank algorithm

The PageRank algorithm was devised in the 1970s by Pinski and Narin (1976) and then popularised in the 1990s to rank web pages according to their ‘popularity’: it was in fact recast as a search–engine ranking algorithm that would prioritise pages with either many incoming web–links or with a few incoming links from highly–ranked pages. In other words, the algorithm assigns a high score not only to those pages that are highly connected, but also to those ones that are linked by popular websites.

In its web application, the model behind the algorithm assumes there is a web-surfer who follows links between web pages and who, after a series of moves, gets bored and lands on a random page. The PageRank of a given page is therefore linked to the probability a random surfer would land to the page. The model can therefore be seen as a Markov process whereby states are pages and the transition probabilities are given by the links among webpages. Therefore, it is not surprising that the calculation of the PageRank is very similar to the derivation of the Markov stationary distribution. Concretely, the equation fixing the PageRank 



where is the unitary

-dimensional vector and where the elements

of the matrix are given by , with the weight matrix of the network considered (see Sec. III.3 above). The quantity is called ‘damping factor’ and is linked to the probability of leaving the current page and landing on a random website. This factor, together with the first term on the rhs of Eq. (5) are included to ensure a transition when landing on a page without out–going links, so to preserve the ergodicity of the process and to ensure the convergence of to a unique stationary density (Masuda et al, 2017). The PageRank is usually computed iteratively, with an initial guess that gets updated by applying Eq. (5) above as:


The result of this computation yields the –dimensional vector that expresses the probability of visiting any given page , i.e., in other words, the ‘popularity’ of that page. In our work, we deal with bibliographic citations among institutions in a fashion akin to links among webpages. In this setting, we effectively derive the popularity of any given institution in a network of citations.

Note that, at odds with other approaches (see, for instance, Pinski and Narin (1976)), we use here an unweighted version of the PageRank algorithm, which does not take into account the total number of publications produced by each institution. We chose this path because the disciplinary areas we analyse are rather compact and the typology (and size) of the different actors is fairly comparable: the size of the institutions considered here is narrowly distributed around the mean, modulo some few extremely small institutions that (we checked) would be over-rewarded by a normalised version of the PageRank algorithm. For this reason, we finally decided to apply an unweighted version of the PageRank algorithm in the present work.

Iv Computing the University reputation via PageRank 

Our aim is to measure aggregate citations from a certain institution to some other academic body, in a given field.

Web of Science Category Unique affil. Publications Citations
Dentistry, Oral Surgery & Medicine 325 33,536 63,701
Business, Finance 434 16,862 27,527
Information Science & Library Science 417 12,488 15,147
Telecommunications 639 47,155 64,812
Veterinary Sciences 330 48,074 50,063
Table 2: A summary of the data analysed. We report here, for each WoS category we analysed, the number of unique affiliations (Unique affil.), of publications (Publications) and of cross-citations (Citations) for the 2010-2014 timespan, after retaining only those affiliations showing a total number of articles above the threshold of the corresponding ARWU-GRAS subject.

To that end, we aggregated all publications at the affiliation level, and were thus able to reconstruct for each WoS category the web of cross–citations among institutions built in the 2010–2014 time period. The resulting system consists of a weighted network , where each node is an academic institution and where weighted edges are the total number of citations occurring within the specific WoS category from publications produced by institution to publications produced by institution .

The network characteristics we obtained for each category are summarised in Table 2, where we report the total number of records, the total number of institutions to which the authors of these publications were affiliated, and the total citations retrieved in the dataset. Fig. 1 shows instead the in–degree centrality distribution of those networks (see Sec. III for details): these statistics allow to get a glimpse of the structure of each network and enable one to understand how connected the hubs in network are.

Figure 1: The in–degree centrality [Eq. (4)] for the five networks considered. These curves show the distribution of citations that institutions receive from peer academic institutions, normalised over the size of the cohort of institutions considered for each WoS category. It appears how Dentistry, Oral Surgery & Medicine and Information Science& Library Science are the most and the least interconnected citation networks, respectively.

For each WoS category analysed, we computed the weighted PageRank of the resulting network of citations. This calculation allowed us to assign a score to each academic institution: this score is related both to ‘quantity’ through the number of aggregated citations any given institution has received and to ‘quality’ according to the provenance of those citations (for more details see Sec. III). The rationale behind our approach is that researchers are expected to cite the most reputable source in their publications and that entities cited by reputed sources are expected to be, in turn, reputable. As a consequence of this mechanism, one expects to be able to quantify academic reputation by measuring citation patterns via PageRank and without the means, for instance, of dedicated surveys.

Note that since PageRank takes into account the reputation of citing institutions, one can circumvent shortcomings due, for instance, to the emergence of ‘citation cartels’ (Franck, 1999), which are not accounted for by mere citation counts. The above phenomenon has been in fact observed in a few instances (Fister et al, 2016), especially in relation to research evaluation schemes that take into account the citations researchers receive (Haley, 2017). Therefore our method allows to strongly discount those effects due to the appearance of clusters of institutions citing each other more frequently than expected.

As we discuss further below in the following subsections, PageRank results are qualitatively in good agreement with the QS thematic rankings for all those areas covered by QS: this first finding suggests that PageRank is indeed capable of reproducing the academic reputation as perceived by surveys, only by means of quantitative methods.

iv.1 Viability of the PageRank algorithm to rank academic institutions

Before getting into deeper analyses, we wanted to assess the soundness of our approach against some well established academic ranking standard. To that aim, we compared the PageRank scores we obtained for each institution in each WoS category with their respective score in the corresponding ARWU-GRAS ranking. ARWU-GRAS rankings are just one possible benchmark of our results. We chose to compare with them because: i. there is a precise mapping that links the different WoS Categories to each ARWU-GRAS Subject, so that we are sure we are comparing apples with apples, and ii. ARWU-GRAS are built on bibliometric indicators computed from the InCites suite, so that the data consistency is ensured.

To carry out our comparisons we started by computing the Pearson and Spearman Correlations, as well as Kendall’s coefficient of concordance between the two scoring systems. Before computing the Pearson Correlation we proceeded to normalise the PageRank scores using the ARWU-GRASS compression procedure explained in Section III.1, i.e. we took the square root of the PageRank scores normalised over the maximum score. Table 3 reports Pearson’s and Spearman’s correlation values, as well as Kendall’s coefficient of concordance between ARWU-GRAS and PageRank scores, while in Fig. 2 we show, for each WoS category, a scatter plot comparing the PageRank  and the ARWU-GRAS normalised scores as well as the citation (CIT) score, which, albeit not considered in ARWU-GRAS could be considered as a viable, simpler alternative to PageRank. According to the results shown in Table 3, we found that ARWU-GRAS scores and PageRank results have a significant correlation. Besides, the concordance between rankings as signaled by the non parametric statistic Kendall’s W, the most familiar measure for concordance (Marozzi, 2014), is strong across the five subjects. However, as one can observe in Fig. 2, ARWU-GRAS (and CIT, for that matter) and PageRank  are by no means interchangeable: the point clouds are in fact rather widely scattered around a trend line at fixed PageRank values.

Research Subject Pearson Spearman Kendall’s W
Dentistry, Oral Surgery & Medicine 0.79 0.74 0.88
Business, Finance 0.87 0.82 0.92
Information Science & Library Science 0.86 0.85 0.91
Telecommunications 0.88 0.85 0.91
Veterinary Sciences 0.89 0.78 0.89
All Pearson’s and Spearman’s correlations are significant ()
Table 3: ARWU-GRAS and PageRank scores correlation coefficients (Pearson and Spearman) and concordance coefficient (Kendall W)
Figure 2: Comparison of the PageRank scores against the ARWU-GRAS and citation (CIT) scores for each WoS category and institution considered in our analysis.

To better understand the source of the observed correlations, and to check to what extent it is due, e.g., to size effects, we performed two further analyses:

  1. we performed a Principal Component Analysis (PCA) on the overall set of the 5 WoS Categories;

  2. we carried out a partial correlation analysis between the PageRank results and the ARWU-GRAS rankings.

Thus, we first merged all WoS categories and performed a Principal Component Analysis (PCA) on the space spanned by the following metrics: Category Normalized Citation Impact (cnci), Total number of publications (pub), Total number of citations (CIT), ARWU-GRAS total score (arwu), PageRank score (pr), and H-index (hindex). The merged dataset we used to perform PCA consists thus of 2145 observation points in a 6–dimensional space, with the correlation matrix shown in Table 4. We retained two principal components which jointly contribute to explain in excess of 89% of the variance in the sample. By projecting the original 6 dimensions onto a reduced 2–dimensional space, one is able to check which of the above metrics lie closer together (i.e. which metrics capture similar features). The result of this effort is shown in Fig.

3, which shows how the metrics listed above lie in the PCA reduced space after varimax rotation. The first and second rotated component explain 61% and 26% of the variance of the sample, respectively.

Indicators arwu prank CIT hindex PUB CNCI
arwu 0.82 0.89 0.80 0.77 0.47
prank 0.82 0.93 0.85 0.68 0.42
CIT 0.89 0.93 0.92 0.79 0.54
hindex 0.80 0.85 0.92 0.65 0.63
PUB 0.77 0.68 0.79 0.65 0.26
CNCI 0.47 0.42 0.53 0.63 0.26
All the correlations are statistically significant ()
Table 4: Correlation matrix corresponding to the variables used for the PC analysis on the whole set of 2145 institutions
Figure 3: A Principal Component Analysis plot that compares of a few metrics with the ARWU-GRAS scores. This plot allows to understand which metrics ARWU-GRAS are better captured by the ranking total score and which one resembles most the PageRank results: the ARWU-GRAS total score (score) is found to be sitting very close to the PageRank results (pra), meaning that PageRank and ARWU-GRAS capture very similar features of the cohort of academic institutions considered. Note that the first component of the plot is mostly aligned with the ‘PUB’ metric, which is tightly correlated with the size of the institution, while the second component is mainly aligned with the size-independent ‘CNCI’ metric: both PageRank and ARWU-GRAS are found to be somewhere in between these two limits.

Surprisingly enough, the PageRank score and the ARWU-GRAS total score are found to be sitting very close together, suggesting that the ARWU-GRAS ranking is effectively capable of capturing the academic reputation of a given institution.

Once we have uncovered and weighed the correlation between ARWU-GRAS and PageRank scores, we would like next to gauge the degree of association between the two rankings by removing a series of controlling underlying variables. To that effect, we have conducted a partial correlation analysis between ARWU-GRAS and PageRank scores, by controlling both for size-dependent (PUB and CIT) and size-independent (CNCI) variables, respectively. The rationale behind partial correlation analysis is that the value of correlation coefficients get reduced insofar as the controlling variable exerts influence in the association between the two ranking schemes.

Controlling for none CNCI PUB CIT
Dentistry, Oral Surgery & Medicine 0.79 0.71 0.42 -0.01
p values
Business, Finance 0.87 0.76 0.42 -0.29
p values
Information Science & Library Science 0.85 0.83 0.52 -0.07
p values
Telecommunications 0.88 0.88 0.65 -0.04
p values
Veterinary Sciences 0.89 0.90 0.70 0.02
p values
Table 5: Partial correlation r-values between ARWU-GRAS and PageRank scores for the five subjects under analysis

The results are shown in Table 5, and contribute to shed a clear light on the relationship between ARWU-GRAS and PageRank:

  • The indicator related to publication impact (CNCI) does not bear any noticeable influence in the relationship between ARWU-GRAS and PageRank scoring. Since CNCI is an integral part of the composed ARWU-GRAS score, this fact stands in support of the difference in which ARWU-GRAS and PageRank acknowledge reputation.

  • The indicator related to the number of publications impact (PUB) exerts a moderate influence in the relationship between ARWU-GRAS and PageRank ranking, signaling that both ARWU-GRAS and PageRank scores are to a certain extent connected with institutional size.

  • Except for Business, Finance, the partial correlations between ARWU-GRAS and PageRank scores do not reach statistical significance when controlling for the indicator CIT. Both methods appear to use up the information provided by the size-dependent indicator related to the total number of citations.

Not surprisingly, by being PageRank built upon citation patterns, the CIT variable is the one conveying most of the correlation between PageRank and ARWU-GRAS. However, results yielded by the two approaches differ markedly when one looks at a finer grain the institutional changes in positions in both classifications. Within each subject, we have computed the absolute value of the change of position of all the institutions as ranked by ARWU-GRAS and PageRank. We then collected relevant descriptive statistics of the so constructed new variables to assess the extent to which PageRank departs from ARWU-GRAS in recognising academic reputation.

Subject N Mean Std.D. p50 p75 p90
DEN 324 48.5 39.4 40 69 108
FIN 434 55.8 48.1 44 81 124
LIB 415 52.2 43.9 42 75 118
TEL 638 77.3 70.2 57 112 181
VET 330 46.3 44.7 30 68 122
Table 6:

Means, standard deviations, and key percentile values

for the absolute value of the differences in position between ARWU-GRAS and PageRank scores.

Table 6 provides, for the five subjects under analysis, means, standard deviations, medians, and a collection of key percentile values for the differences (absolute value) in position in ARWU-GRAS and PageRank of all the institutions. We have included the median as well as percentiles 75 and 90 which will point to the behavior of the ranking different for the or of the institutions, respectively. The results shown in Table 6 reveal large position swaps in both classifications (e.g., by computing the ratio of the the last column (P90) over the second one (N), we realize that of the institutions in each subject suffer a rank change of about of the total number of universities included in the sample), uncovering differences in both ranking methodologies in spite of the observed significant correlations between them.

These differences highlight how PageRank is a useful protocol to capture reputation rather than impact: indeed, while citations set a common trend between ARWU-GRAS and PageRank results (hence the observed correlations), with PageRank it does not only matter how many citation an institution aggregates, but also the provenance of them.

A finer analysis is presented for each single WoS Category in the next few sections, where we also show and discuss briefly the properties of each resulting citation network.

iv.2 Dentistry, Oral Surgery & Medicine

The institutional citation network for Dentistry, Oral Surgery & Medicine is relatively dense, compared with the other cases analysed further below: indeed, the in–degree centrality distribution of this particular network has a much fatter tail than those emerging from the other categories, as shown in Fig. 1. This is also consistent with the numbers reported in Table 2, where the citations–to–institutions ratio is the highest. This characteristic is intrinsically related with the citation habits of the disciplines within the field of the Health and Medical Sciences, where papers usually include more references as compared to other disciplines (Marx and Bornmann, 2014; Crespo et al, 2013).

This feature makes, in turn, the institutional network more interconnected, since more references per publication directly imply more affiliations cited. This is clearly visible by looking at the actual shape of the network analysed in Fig. 4: the network edges (i.e. citations from one institution to another) cover indeed the whole background space.

Figure 4: The institutional network of cross-citations in the Dentistry, Oral Surgery & Medicine WoS category. Edges are citations from a publication produced by an institution to those authored by another one (10% of the total edges are plotted). The node size is proportional to the number of publications.

Despite the overall large connectivity, a cluster of central hub institutions (in terms of in–degree centrality) can be easily detected in the network: these are the institutions receiving more citations from their peers and are the ones for which we show names in Fig 4.

PageRank ARWU–GRAS rank Institution
1 9 University of Sao Paulo
2 26 University of Gothenburg
3 17 University of Bern
4 40 UNESP
5 1 University of Michigan-Ann Arbor
6 44 University of Campinas
7 50 The University of Hong Kong
8 41 Academic Center for Dentistry Amsterdam
9 25 University of Zurich
10 4 Harvard University
Table 7: The Top 10 PageRank institutions in Dentistry, Oral Surgery & Medicine. According to the PageRank metrics and definitions, these are the most ‘reputable’ academic institutions in this WoS category. The three columns show the position in the PageRank, the classification in the ARWU-GRAS (computed according to Docampo (2013)) and the academic institution, respectively.

When computing the PageRank, we find as the best scored the University of São Paulo, the University of Gothenburg and the University of Bern, respectively. These institutions are also among the central core of the citation network, meaning that those universities not only receive many citations, but they also do so from equally prestigious institutions. The list of the top 10 institutions for reputation measured via PageRank is given in Table 7, while the full list is provided in the Supplementary material. Importantly, out of these ten institutions, four of them (the University of Michigan, the University of Hong Kong, the Academic Center for Dentistry Amsterdam and Harvard University, respectively) are featured in the top 10 universities of the QS thematic ranking for Dentistry, in terms of the metric academic reputation.

One can also compute the Spearman’s correlation coefficient on total scores between QS and ARWU GRAS and PageRank, for the the top 20 institutions of the QS Subject ranking. By doing so, one sees that the Spearman coefficient between QS and ARWU is , while between QS and PageRank is . These facts suggest PageRank is actually capable of capturing the reputation of a given institution, as expected, and to go beyond ARWU GRAS results. But while the academic reputation score in the QS ranking is obtained by means of surveys, whose control in terms of significance and robustness is hard to attain, here we derived a similar score based only on bibliometric data.

iv.3 Business, Finance

The second category we study is Business, Finance. This network is the second smallest in terms of publications and citations, suggesting there exist a fragmentation pattern in knowledge communication and sharing in this field. By looking at the actual shape of the network in Fig. 5, one can appreciate that ‘peripheral’ nodes are effectively little connected but that, at the same time, there is a densely inter–connected cluster of hub institutions at the center. The in–degree centrality distribution shown in Fig. 1 has indeed a fatter tail distribution in this case if compared with the Information Science & Library Science WoS category, for instance.

Figure 5: The institutional network of cross-citations in the Business, Finance WoS category. Edges are citations from a publication produced by an institution to those authored by another one (10% of the total edges are plotted). The node size is proportional to the number of publications.
PageRank ARWU–GRAS rank Institution
1 2 University of Pennsylvania
2 1 New York University
3 15 Stanford University
4 4 Harvard University
5 3 University of Chicago
6 29 Northwestern University
7 6 Massachusetts Institute of Technology (MIT)
8 20 Duke University
9 5 Columbia University
10 12 University of Michigan-Ann Arbor
Table 8: The Top 10 PageRank institutions in Business, Finance. According to the PageRank metrics and definitions, these are the most ‘reputable’ academic institutions in this WoS category. The three columns show the position in the PageRank, the classification in the ARWU-GRAS (computed according to Docampo (2013)) and the academic institution, respectively.

The PageRank results are, once again, fairly interesting. In the top three position, we find the University of Pennsylvania, the New York University and Stanford University, respectively. The three of them belong to the central cluster of knowledge hub institutions. The list of top 10 institutions is given in Table 8, while the full rank is given in the Supplementary Materials. In the top 10 we find 6 institutions (Harvard University, and Stanford University, Massachusetts Institute of Technology, University of Chicago, University of Pennsylvania, and New York University, respectively) that are featured in the first 10 positions in the QS Accounting and Finance thematic ranking, for academic reputation. Also, considering the top 20 institutions featured in the QS Subject ranking, the Spearman’s correlation coefficient between the QS scores and ARWU GRAS scores is , while for the case of PageRank it equals . Again, these findings are a strong indication that PageRank is indeed capable of capturing the reputation of an Academic institution based solely on bibliometric data and to go beyond ARWU results.

iv.4 Information Science & Library Science

We next examined the case of Information Science & Library Science, which is of special interest for us, considering the focus of the present work. This field is the one with the fewer publications and citations with respect to the set of WoS Categories analysed in this paper. The corresponding network shown in Fig. 6 is indeed much sparser than the previous ones and the respective in–degree centrality distribution decays much faster than in the other cases (see Fig. 1).

Figure 6: The institutional network of cross-citations in the Information Science & Library Science WoS category. Edges are citations from a publication produced by an institution to those authored by another one (10% of the total edges are plotted). The node size is proportional to the number of publications.

In this case, there is no sharp cluster of central hubs of knowledge as in the previous cases, albeit some key nodes (in terms of in–degree centrality) can be identified in the core of the network (as, e.g., the Indiana University at Bloomington, the University of Amsterdam and the National University of Singapore). However, these are not as interconnected as the central core nodes in the Dentistry case, for instance. This finding suggests that the research communities in the field of Information Science & Library Science are much more fragmented than in the other fields of research considered in the present study.

PageRank ARWU–GRAS rank Institution
1 8 University of Maryland, College Park
2 1 Harvard University
3 2 Indiana University Bloomington
4 6 University of Amsterdam
5 17 Leiden University
6 5 KU Leuven
7 14 University of Washington
8 56 Temple University
9 34 University of British Columbia
10 32 University of Pittsburgh, Pittsburgh Campus
Table 9: The Top 10 PageRank institutions in Information Science & Library Science. According to the PageRank metrics and definitions, these are the most ‘reputable’ academic institutions in this WoS category. The three columns show the position in the PageRank, the classification in the ARWU-GRAS (computed according to Docampo (2013)) and the academic institution, respectively.

The PageRank analysis in this particular network yields the results shown in Table 9. We find the University of Maryland (College Park), Harvard University and the Indiana University at Bloomington, respectively, to be the three most reputable institutions in terms of citations measured via PageRank. The full top 10 rank of this category is reported in Table 9, while the full list is provided in the Supplementary Materials.

In this case, there is no specific QS thematic ranking to compare with, but the results we obtain seem to be very meaningful without much further checking: we find indeed all the ‘usual suspects’ to be in the top 10 of the ranking. It is noteworthy the compactness of the northern European group, University of Amsterdam–Leiden–KU Leuven: these universities are clearly a world reference and thus high in the PageRank because of their global impact. However, by frequently cross–citing each other, they produce a supplementary ‘catalysing effect’ which boosts their score towards the top of the ranking.

iv.5 Telecommunications

The next case study is the Telecommunications WoS category. This particular network is densely connected and it is hard to detect a central cluster of emerging institutions. A clear, emerging feature is, in this case, the massive presence of Chinese institutions. The top 3 institutions according to the PageRank metrics are the University of Texas at Austin, Tsinghua University and the University of Waterloo, respectively. It is worth noting in this case that not all the top PageRank institutions are the ones with the largest in–degree centrality: indeed, a large number of citations does not necessarily imply a high PageRank, meaning that the reputation of the citing institutions and not only the quantity of citations matters in PageRank.

Figure 7: The institutional network of cross-citations in Telecommunications. Edges are citations from a publication produced by an institution to those authored by another one (10% of the total edges are plotted). The node size is proportional to the number of publications.

Albeit there is no QS reputation specific ranking corresponding to this category, the top institutions we found are all featured in the top positions of the ARWU-GRAS ranking of Telecommunication Engineering. The list of the top 10 institutions is given Table 10, while the full results are provided in the Supplementary Materials.

PageRank ARWU–GRAS rank Institution
1 4 The University of Texas at Austin
2 1 Tsinghua University
3 6 University of Waterloo
4 8 Georgia Institute of Technology
5 2 Beijing University of Posts and Telecommunications
6 7 Xidian University
7 3 Nanyang Technological University
8 5 University of British Columbia
9 12 Southeast University
10 10 Shanghai Jiao Tong University
Table 10: The Top 10 PageRank institutions in the Telecommunication Engineering WoS category. According to the PageRank metrics and definitions, these are the most ‘reputable’ academic institutions in this WoS category. The three columns show the position in the PageRank, the classification in the ARWU-GRAS (computed according to Docampo (2013)) and the academic institution, respectively.

iv.6 Veterinary Sciences

The last WoS category we analyse is Veterinary Sciences. Again, the resulting network is fairly connected: in this case as well it is possible to detect a central cluster of ‘influencing’ institutions, that – interestingly – belong to very different geographical regions. Among those, we may name for instance the University of California at Davis, the Royal Veterinary College and Ghent University.

Figure 8: The institutional network of cross-citations in the Veterinary Sciences WoS category. Edges are citations from a publication produced by an institution to those authored by another one (10% of the total edges are plotted). The node size is proportional to the number of publications.

We computed the PageRank for the last time on this particular network and found the top three universities to be the University of California at Davis, the Royal Veterinary College and the University of Pennsylvania: therefore, some of the main hubs in this case do also correspond with the most reputable institutions in the field. The top 10 PageRank institutions in this category are listed in Table 11, while the full list is provided in the Supplementary Materials.

PageRank ARWU–GRAS rank Institution
1 5 University of California, Davis
2 3 The Royal Veterinary College
3 19 University of Pennsylvania
4 1 Ghent University
5 18 Cornell University
6 10 Colorado State University
7 26 University of Guelph
8 14 University of Copenhagen
9 13 Swedish University of Agricultural Sciences
10 29 North Carolina State University - Raleigh
Table 11: The Top 10 PageRank institutions in the Veterinary Sciences WoS category. According to the PageRank metrics and definitions, these are the most ‘reputable’ academic institutions in this WoS category. The three columns show the position in the PageRank, the classification in the ARWU-GRAS (computed according to Docampo (2013)) and the academic institution, respectively.

We can again compare the results we obtain for the top listed institutions with the QS thematic ranking in Veterinary. Five of the institutions listed in Table 11 are found to be in the top 10 QS Veterinary when ranked for academic reputation (in particular, the first two are found to coincide), a fact that hints again that PageRank is indeed capable of capturing the reputation of a certain institution. Furthermore, when computing the Spearman’s correlation coefficient on total scores between QS and ARWU GRAS and PageRank, for the the top 20 institutions of the QS Subject ranking, one finds between QS and ARWU and between QS and PageRank, again implying that PageRank results enables one to gain more information than merely using ARWU GRAS rankings.

V Discussion

We began this paper by recognising the intrinsic difficulty in measuring academic reputation, either through carefully designed surveys or the use of proxy indicators of performance. In spite of the complexity of academic institutions and their varied missions, we came to stress the pre-eminence of research performance as the main measurable source of academic reputation, due to the fact that scientific progress is nowadays judged through well-established peer review processes and research outcomes are easily measured in terms of numbers of publications and citations in the scientific literature.

We have then discussed the relationship between reputation and the emergence of academic rankings. Academic classifications come in different flavours, but, as we argue, they can be reduced to two main categories according to the role they assign to quantitative data. We have acknowledged two well-established worldwide rankings, THE and QS, which allocate a considerable weight to reputation scores based on academic surveys, and the ARWU-GRAS ranking, driven by publication and staff figures.

Academic rankings try to gauge institutional quality and in so doing become academic performance referees. However, one should never forget that measurements in the social sciences are a tricky business. In this regard, Adcock and Collier have sagely recommended to try and draw a clear distinction between measurement issues and disputes about concepts (Adcock and Collier, 2002). International classifications arguably raise controversy about both sides of the issue, that is to say, about whether the used indicators constitute valid measurements of academic performance, or whether the reputation they claim to recognise is tantamount to university excellence. Ultimately, even though reputation is built upon the perception of quality, which should, to a reasonable extent, be captured by THE and QS, it is not beyond any reasonable doubt that their surveys are not prone to biases, not the least of them being the difficulty for newcomers to play the reputation game.

Yet, we argue in the paper that researchers have put in place a solid and measurable way to credit reputation through citations to the published work of other researchers. Hence, we reckon that there is a wealth of information at our disposal to assess reputation levels using solid data, thus dodging the biases associated with any, no matter how carefully and thoroughly designed, survey.

We acknowledge that a good ‘reputation attribution’ mechanism has been successfully embedded in the PageRank algorithm, which has been widely popularised as a ranking tool for the relevance of web pages. In our study, we substitute citations among universities for links to web pages. The rationale behind our approach is that scientific citations are driven by the quality of the cited work, and that, in turn, citations become the more relevant the higher the reputation of the citing institution. To put it plainly, researchers are expected to cite reputable sources in their publications; in turn, institutions cited by reputed sources are expected to be reputable. The PageRank algorithm handles the trade-off between quantity of citations and relevance of their sources and thus becomes a reliable instrument to assess the reputation of an academic center in a specific field. For this project, we have computed the results of the PageRank algorithm within a network of institutions citing one another through scientific papers published between 2010 and 2014; as a result, we have been able to render a picture of a research subject in which institutions become ranked by reputation through analyzing the number and provenance of their citations.

We built institutional citation networks for five WoS categories: networks are shown in Figs. 48, where each node represent an academic institution and edges citations from one institution to another. For each network, we uncovered different structural properties (as summarised, for instance, in Fig. 1), due to different citing habits in the different research fields considered. Additionally, by using standard network metrics, we were able to detect particularly central institutional nodes in each research field network.

We assessed the soundness of our analyses by comparing PageRank results to a well established academic ranking standard: because we have selected for our analysis five WoS categories each matching exactly one ARWU-GRAS, we have been able to relate our measurements of academic reputation via PageRank to the score the same institutions attain in the equivalent ARWU-GRAS ranking. Moreover, since three of the subjects selected also correspond to three Thematic QS subjects, we have also been able to analyze the level of association of reputation via QS surveys with measures taken using the PageRank algorithm.

We found a statistically significant association between the results rendered by the PageRank algorithm and the Shanghai Subject Rankings corresponding to the the five WoS categories analysed in the paper. The correlation between both measurements was found to be very solid, as Table 3 shows. We also computed the correlation of ARWU-GRAS and PageRank results with four widely used bibliometric indicators, namely the number of publications, the number of citations, the H-index, and the category normalized citation impact to have a calibration measure to weigh the significance of PageRank  and ARWU-GRAS correlation. The general agreement of PageRank scores with ARWU-GRAS scores and the four bibliometric indicators stands in support of the reliability and validity of our results.

Furthermore, we explored the locus of the PageRank indicator in relation to the ARWU-GRAS score, when the two measures were part of a multi-dimensional space along with the four measures included with ARWU-GRAS and PageRank in 4 that bear a close connection with quantity and quality of publications: from raw quantity in number of publications (PUB) and citations (CIT), to impact (CNCI), through a well-known measure that effectively combines quantity and quality, the H-index (see Table 4). The four measures (PUB, CIT, CNCI, H-index) have been computed for the set of articles published by each institution between 2010 and 2014, within the subject under analysis. Citations were counted up until October 2017. The merged dataset contained 2145 observations. By reducing the dimensionality using principal components we were able to elucidate the answer to the quest for the locus of the PageRank results, since two principal components, which jointly contribute to explain in excess of 89% of the variance of the sample, were enough to show the metrics that capture similar features. As shown in Figure 3, the position of the PageRank score and the ARWU-GRAS total score in the two-dimensional principal components offer compelling evidence of the level of concordance of ARWU-GRAS and PageRank. Moreover, in the three WoS categories selected for the analysis with equivalent subject QS rankings, we found that the results from the PageRank algorithm were in fact very well aligned with the perceived reputation as gauged by the QS surveys.

As we have pointed out in Sec. IV through partial correlation analyses, the association between ARWU-GRAS and PageRank scores is mainly due to the size-dependent variable controlling for the total number of citations. However, we showed that actual ranking results vary deeply when ranking institutions for citations or via PageRank. This is because PageRank accounts both the number of citations to score institutions (hence the correlation) and the provenance of them (hence the observed difference). This finding stands in support of the adoption of PageRank as a protocol for measuring reputation: indeed, an institution is reputable not only if it is highly cited, but also if it is cited by reputable institutions. In other words, with PageRank, it is not only important how many people cite my work, but also who these people are.

By pointing to the similarities between ARWU-GRAS, QS and PageRank, we meant to demonstrate the reliability and validity of our approach. However, the magnitude of the correlation between ARWU-GRAS and PageRank does not mean that their scores “convey the same information, and thus can be used interchangeably” (West et al, 2010): to show the degree of departure of a PageRank driven classification from the one provided by ARWU-GRAS we analysed the institutional changes in positions in both rankings. After computing the changes (absolute value), the descriptive statistics shown in Table 6 uncovered substantial differences in the way ARWU-GRAS and PageRank acknowledge reputation.

Vi Conclusion and Further Work

In this paper, we have explored the use of a quantitive, and reliable proxy, the PageRank algorithm, to assess academic reputation through the analysis of citation patterns among universities. For the analysis we have selected five different Web of Science categories, corresponding to research subjects studied by well established international academic classifications.

To support the soundness of our work, we have supplied compelling evidence about the close connection of the results of the PageRank algorithm with the scores on two rankings that handle academic reputation in two distinct ways: scores partially based on academic surveys, QS, or driven by publication and staff figures, ARWU-GRAS. These two academic rankings do have their shortcomings: for instance, ARWU suffers limitations due to aggregation methodologies and with the chosen criteria (Billaut et al, 2010), while the return rate of the reputational surveys and reliability of the statistical data were questioned for QS (Huang, 2012; Bookstein et al, 2010). However, although those two ranking methodologies do not enjoy the favour of the whole academic community, we believe they – at least superficially – capture some features of academic excellence. Because in this work we are proposing a new framework for ranking academic institutions, we therefore felt necessary to compare our results with those two standards, despite the controversy they both stir.

The fact that the PageRank algorithm operates with hard data obtained through the analysis of citation patterns among papers published in peer review publications, well rooted, therefore, in a sound and credible mechanism for recognising reputation among researchers and institutions, makes PageRank a very reasonable candidate to be used as a direct mean to assess academic reputation. We believe that the results from our paper provide a solid argument in favor of using PageRank scores based only on bibliometric instead of (or along with) estimations through surveys to measure academic reputation.

The current study offers a new glimpse into the analysis of scientific reputation via PageRank that adds to the large body of literature in which network analysis and bibliometrics are combined. The work carried out in this study may be extended along several research lines, by exploiting a few Network Science techniques. In this sense, a few interesting research venues we are planning to explore are for instance related to the measurement of the ‘boost’ in Academic Prestige due to institutional self–citations (which is expected to enhance the PageRank score and, thus, to correlate positively with academic ranking scores), to the detection of communities (i.e. of groups of academic institutions cross-citing each other more frequently than expected) and, finally, to the relation of the above two properties to the geographical location of the analysed institutions.


The authors would like to thank Clarivate Analytics for kindly providing all the information needed to replicate the results of the PageRank algorithm in the five WoS categories selected for this project. The authors would also like to thank Solange Chavel, Enric Fuster and Sebastian Stride for useful comments and suggestions. FAM is supported by the Spanish MINECO grant PTQ-14-06718 of the Torres Quevedo programme. DD is supported by the European Regional Development Fund (ERDF) and the Galician Regional Government under agreement for funding the Atlantic Research Center for Information and Communication Technologies (atlanTTIC).



  • Adcock and Collier (2002) Adcock R, Collier D (2002) Measurement validity: A shared standard for quantitative and qualitative research. The American Political Science Review, 95(3), 529–546.
  • Aghion et al (2008) Aghion P, Dewatripont M, Hoxby C, Mas-Colell A, Sapir A (2008) Higher aspirations: An agenda for reforming european universities: Bruegel blueprint V., downloaded from on March 2nd 2013.
  • Aguillo et al (2010) Aguillo IF, Bar-Ilan J, Levene M, Ortega JL (2010) Comparing university rankings. Scientometrics 85(1):243–256.
  • ARWU (2017) ARWU (2017) ARWU-GRAS mapping of Web of Science categories.
  • Bergstrom (2007) Bergstrom C (2007) Eigenfactor: Measuring the value and prestige of scholarly journals. College & Research Libraries News 68(5):314–316.
  • Billaut et al (2010) Billaut JC, Bouyssou D, Vincke P (2010) Should you believe in the Shanghai ranking? Scientometrics 84(1):237–263.
  • Bookstein et al (2010) Bookstein FL, Seidler H, Fieder M, Winckler G (2010) Too much noise in the Times Higher Education rankings. Scientometrics 85(1):295–299.
  • Brin and Page (1998) Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems 30(1):107 – 117., proceedings of the Seventh International World Wide Web Conference.
  • Crespo et al (2013) Crespo JA, Li Y, Ruiz-Castillo J (2013) The measurement of the effect on citation inequality of differences in citation practices across scientific fields. PLOS ONE 8(3):1–9.
  • CWTS (2017) CWTS (2017) Leiden Ranking 2017 – Moving beyond just ranking
  • Dehon et al (2010) Dehon C, McCathie A, Verardi V (2010) Uncovering excellence in academic rankings: a closer look at the Shanghai ranking. Scientometrics, 83(2), 515–524.
  • Ding et al (2009) Ding Y, Yan E, Frazho A, Caverlee J (2009) Pagerank for ranking authors in co-citation networks. Journal of the American Society for Information Science and Technology 60(11):2229–2243.
  • Docampo (2011) Docampo D (2011) On using the shanghai ranking to assess the research performance of university systems. Scientometrics 86(1):77–92.
  • Docampo (2013) Docampo D (2013) Reproducibility of the Shanghai academic ranking of world universities results. Scientometrics 94(2):567–587.
  • Docampo and Cram (2014) Docampo D, Cram L (2014) On the Internal Dynamics of the Shanghai ranking. Scientometrics 98(2):1347–1366.
  • Dunaiski et al (2016) Dunaiski M, Visser W, Geldenhuys J (2016) Evaluating paper and author ranking algorithms using impact and contribution awards. Journal of Informetrics 10(2):392 – 407.
  • Fister et al (2016) Fister I, Fister I, Perc M (2016) Toward the discovery of citation cartels in citation networks. Frontiers in Physics 4:49.
  • Foulley et al (2017) Foulley JL, Celeux G, Josse J (2017) Empirical bayes approaches to pagerank type algorithms for rating scientific journals. ArXiv e-prints eprint 1707.09508
  • Franck (1999) Franck G (1999) Scientific communication–a vanity fair? Science 286(5437):53–55.
  • Gao et al (2016) Gao C, Wang Z, Li X, Zhang Z, Zeng W (2016) Pr-index: Using the h-index and pagerank for determining true impact. PLOS ONE 11(9):1–13.
  • Ghasemian et al (2018) Ghasemian F, Zamanifar K, Ghasem-Aghaee N (2018) An evolutionary non-linear ranking algorithm for ranking scientific collaborations. Applied Intelligence 48(2):465–481.
  • Haley (2017) Haley MR (2017) On the inauspicious incentives of the scholar-level h-index: an economist’s take on collusive and coercive citation. Applied Economics Letters 24(2):85–89.
  • Hazelkorn (2018) Hazelkorn E (2018) Reshaping the world order of higher education: the role and impact of rankings on national and global systems. Policy Reviews in Higher Education 0(0):1–28.
  • Huang (2012) Huang MH (2012) Opening the black box of qs world university rankings. Research Evaluation 21(1):71–78.,
  • Jeremic et al (2011) Jeremic V, Bulajic M, Martic M, Radojicic Z (2011) A fresh approach to evaluating the academic ranking of world universities. Scientometrics 87(3):587–596.
  • Kazi et al (2016) Kazi P, Patwardhan M, Joglekar P (2016) Towards a new perspective on context based citation index of research articles. Scientometrics 107(1):103–121.
  • Lages et al (2016) Lages J, Patt A, Shepelyansky D (2016) Wikipedia ranking of world universities. The European Physical Journal B 89(3):69.
  • Liu and Cheng (2005) Liu NC, Cheng Y (2005) The academic ranking of world universities: Methodologies and problems. Higher Education in Europe, 30(2):127–136.
  • Lu et al (2009) Lu B, Shi S, Ma Y, Wen JR (2009) Nonlinear algorithms for static-rank computation. Tech. rep.
  • Luo et al (2018) Luo F, Sun A, Erdt Mea (2018) Exploring prestigious citations sourced from top universities in bibliometrics and altmetrics: a case study in the computer science discipline. Scientometrics 114(1):1–17.
  • Marozzi (2014) Marozzi M (2014) Testing for concordance between several criteria. Journal of Statistical Computation and Simulation 84(9):1843–1850.
  • Marx and Bornmann (2014) Marx W, Bornmann L (2014) On the causes of subject-specific citation rates in Web of Science. ArXiv e-prints eprint 1408.5893
  • Masuda et al (2017) Masuda N, Porter MA, Lambiotte R (2017) Random walks and diffusion on networks. Physics Reports 716-717:1–58., random walks and diffusion on networks
  • Moed (2017) Moed HF (2017) A critical comparative analysis of five world university rankings. Scientometrics 110(2):967–990.
  • Moed et al (2012) Moed HF, Colledge L, Reedijk J, Moya-Anegon F, Guerrero-Bote V, Plume A, Amin M (2012) Citation-based metrics are appropriate tools in journal assessment provided that they are accurate and used in an informed way. Scientometrics 92(2):367–376.
  • NTU (2017) NTU (2017) Performance Ranking of Scientific Papers for World Universities
  • Nykl et al (2014) Nykl M, Ježek K, Fiala D, Dostal M (2014) Pagerank variants in the evaluation of citation networks. Journal of Informetrics 8(3):683–692.
  • Pinski and Narin (1976) Pinski G, Narin F (1976) Citation influence for journal aggregates of scientific publications: Theory, with application to the literature of physics. Information Processing and Management 12:297–312.
  • Radicchi (2011) Radicchi F (2011) Who is the best player ever? a complex network analysis of the history of professional tennis. PLOS ONE 6(2):1–7.
  • Radicchi et al (2009) Radicchi F, Fortunato S, Markines B, Vespignani A (2009) Diffusion of scientific credits and the ranking of scientists. Phys Rev E 80:056,103
  • Rizkallah and Sin (2010) Rizkallah J, Sin DD (2010) Integrative approach to quality assessment of medical journals using impact factor, eigenfactor, and article influence scores. PLoS ONE 5(4):e10,204
  • Salmi (2009) Salmi J (2009) The challenge of establishing directions in development. World Bank. license: Cc by 3.0 igo
  • Senanayake et al (2015) Senanayake U, Piraveenan M, Zomaya A (2015) The pagerank-index: Going beyond citation counts in quantifying scientific impact of researchers. PLOS ONE 10(8):1–34.
  • SJR (2007) SJR (2007) Scimago journal & country rank
  • Vernon et al (2018) Vernon M, Balas E, Momani S (2018) Are university rankings useful to improve research? a systematic review. PLoS ONE 13(3):e0193,762
  • Waltman et al (2012) Waltman L, Calero-Medina C, Kosten J, Noyons ECM, Tijssen RJW, van Eck ea N J (2012) The Leiden ranking 2011/2012: Data collection, indicators, and interpretation. Journal of the American Society for Information Science and Technology, 63(12), 2419–2432.
  • West et al (2010) West JD, Bergstrom TC, Bergstrom CT (2010) Big macs and eigenfactor scores: Don’t let correlation coefficients fool you. JASIST 61(9):1800–1807.
  • Yates and Dixon (2015) Yates EJ, Dixon LC (2015) Pagerank as a method to rank biomedical literature by importance. Source Code for Biology and Medicine 10(1):16.
  • Zhang (2016) Zhang F (2016) Evaluating journal impact based on weighted citations. Scientometrics 113(2):1155–1169.