Ranking scientific publications using a model of network traffic

Walker, Dylan; Xie, Huafeng; Yan, Koon Kiu; Maslov, Sergei

doi:10.1088/1742-5468/2007/06/p06010

Cited by 210 publications

(230 citation statements)

References 9 publications

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“…1f), indicating that the top-ranked node remains the same for each network configuration, being insensitive to perturbations. Equation (4) predicts that for an exponential network the gap between consecutive pageranks never exceeds the fluctuations, making the ranking rather sensitive to perturbations. In contrast, according to equation (3), for certain (N, γ and α) combinations in a scale-free network the stability criteria equation (2) is satisfied, predicting the existence of a finite set of nodes whose ranking is stable to network perturbations.…”

Section: Resultsmentioning

confidence: 99%

Ranking stability and super-stable nodes in complex networks

2011

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Pagerank, a network-based diffusion algorithm, has emerged as the leading method to rank web content, ecological species and even scientists. Despite its wide use, it remains unknown how the structure of the network on which it operates affects its performance. Here we show that for random networks the ranking provided by pagerank is sensitive to perturbations in the network topology, making it unreliable for incomplete or noisy systems. In contrast, in scale-free networks we predict analytically the emergence of super-stable nodes whose ranking is exceptionally stable to perturbations. We calculate the dependence of the number of super-stable nodes on network characteristics and demonstrate their presence in real networks, in agreement with the analytical predictions. These results not only deepen our understanding of the interplay between network topology and dynamical processes but also have implications in all areas where ranking has a role, from science to marketing.

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Section: Resultsmentioning

confidence: 99%

Ranking stability and super-stable nodes in complex networks

2011

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“…Nor do we consider indices accounting for the quality of the citations in terms of the collaboration distance between citing and cited authors [47], as we do not model the presence of research groups. Finally, we do not consider metrics based on the eigenvector centrality within the citation network [48], such as PageRank [49], CiteRank [49], or PhysAuthorRank [50]. This is because the linking probability defined by Eq.…”

Section: Impact Indicatorsmentioning

confidence: 99%

Model-based evaluation of scientific impact indicators

Medo

Cimini

2016

Phys. Rev. E

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Using bibliometric data artificially generated through a model of citation dynamics calibrated on empirical data, we compare several indicators for the scientific impact of individual researchers. The use of such a controlled setup has the advantage of avoiding the biases present in real databases, and it allows us to assess which aspects of the model dynamics and which traits of individual researchers a particular indicator actually reflects. We find that the simple average citation count of the authored papers performs well in capturing the intrinsic scientific ability of researchers, regardless of the length of their career. On the other hand, when productivity complements ability in the evaluation process, the notorious h and g indices reveal their potential, yet their normalized variants do not always yield a fair comparison between researchers at different career stages. Notably, the use of logarithmic units for citation counts allows us to build simple indicators with performance equal to that of h and g. Our analysis may provide useful hints for a proper use of bibliometric indicators. Additionally, our framework can be extended by including other aspects of the scientific production process and citation dynamics, with the potential to become a standard tool for the assessment of impact metrics.

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“…Variants of the HITS [13] and PageRank [20] algorithms were employed for scoring objects in different applications [11,19,12,26]. In particular, relevance propagation models for expert search were studied in e-mail collections by Dom, et al [7] and enterprise collections by Serdyukov, et al [22].…”

Section: Related Workmentioning

confidence: 99%

Ranking authors in digital libraries

Gollapalli

Mitra

Giles

2011

Proceedings of the 11th Annual International ACM/IEEE Joint Conference on Digital Libraries

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Searching for people with expertise on a particular topic also known as expert search is a common task in digital libraries. Most models for this task use only documents as evidence for expertise while ranking people. In digital libraries, other sources of evidence are available such as a document's association with venues and citation links with other documents. We propose graph-based models that accommodate multiple sources of evidence in a PageRank-like algorithm for ranking experts. Our studies on two publiclyavailable datasets indicate that our model despite being general enough to be directly useful for ranking other types of objects performs on par with probabilistic models commonly used for expert ranking.

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Ranking scientific publications using a model of network traffic

Cited by 210 publications

References 9 publications

Ranking stability and super-stable nodes in complex networks

Ranking stability and super-stable nodes in complex networks

Model-based evaluation of scientific impact indicators

Ranking authors in digital libraries

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