Unified Author Ranking based on Integrated Publication and Venue Rank (original) (raw)

AuthorRank: A New Scheme for Identifying Field-Specific Key Researchers

2015

When navigating into a new research field, it is important to identify papers with greatest impact and prominent authors which we can refer to. This work is motivated by the need to identify key authors in research fields. Traditional indices such as h-index only show the overall performance of an author. However, researchers generally contribute to more than one fields of research in their career, which makes it impractical to use h-index for identifying a key researcher in a research field. In this paper we propose a new PageRank-based scheme named “AuthorRank” for identifying key researchers in a specific field. We show that the proposed ranking system performs better than h-index does.

A comprehensive author ranking evaluation of network and bibliographic indices

2020

With the immense growth of scientific literature over the Web, the authors of research papers are being ranked for various purposes such as for being shortlisted for different scientific awards, for prestigious position, for tenured appointments, for keynote speaker invitation or for allocation of research grants. The traditional paradigm to attain these aspects is based on bibliometric indices, such as publication count, citation count and h-index. The quintessential indicator among all these indices are based on a number of citations received by the publication of an author. Generally, when a research paper is published, it receives citations after some time and may take more than a couple of years to attain a reasonable number of received citations which could make a difference in researcher ranking based on bibliometric parameters. Therefore, dependability over these bibliometric indices for authors ranking is not beneficial for researchers at the start of their academic career....

A novel approach to Rank Authors in an Academic Network

Ranking the authors in an academic network is a significant research domain to find the top authors in various domains. We find various links based ranking algorithms and index based approaches to measure the productivity and impact of an author in a social network of authors. The research problem to rank the experts has vast applications such as advisor finding, domain expert identification. In this paper, we propose a novel approach to rank the scholars in the academic network of DBLP, a well-known computer science bibliography website. A huge data set is prepared covering the publications of more than 70 years. We propose AuthorRank and Weighted AuthorRank algorithms based on the state of the art ranking algorithms of PageRank and weighted PageRank algorithms respectively. For weighted algorithms, existing methods lack to provide diverse weights. We introduce the novel weights of h-index, g-index and R-index and elaborate their impact to identify the top authors in the scholarly network. The results confirm that the proposed algorithms find the top authors in an effective manner.

Generalized comparison of graph-based ranking algorithms for publications and authors

Journal of Systems and Software, 2006

Citation analysis helps in evaluating the impact of scientific collections (journals and conferences), publications and scholar authors. In this paper we examine known algorithms that are currently used for Link Analysis Ranking, and present their weaknesses over specific examples. We also introduce new alternative methods specifically designed for citation graphs. We use the SCEAS system as a base platform to introduce these new methods and perform a generalized comparison of all methods. We also introduce an aggregate function for the generation of author ranking based on publication ranking. Finally, we try to evaluate the rank results based on the prizes of 'VLDB 10 Year Award ', 'SIGMOD Test of Time Award ' and 'SIGMOD E.F.Codd Innovations Award '. 1 We have built a web-based library called SCEAS (standing for Scientific Collection Evaluator by Advanced Scoring) with data extracted from DBLP, and accessible through the url: http://delab.csd.auth.gr/sceas.

From CiteSeer to CiteSeerX: Author rankings based on coauthorship networks

CiteSeer was a digital library and a search engine gathering its mainly computer science research papers from the World Wide Web. After a few years of stagnation, it was definitely replaced with a new version called CiteSeer X in April 2010. As both CiteSeers provide(d) freely available metadata on the articles they index(ed), it is possible to analyze two different data sets to see the differences between CiteSeer and CiteSeer X . More specifically, we examined the article metadata from CiteSeer (downloaded in December 2005) and from CiteSeer X (harvested in March 2011) with a view of creating rankings of prestigious computer scientists. Since the free article metadata acquired from the Web site of CiteSeer X differ from those in CiteSeer in that they do not systematically include cited references, the only possibility of creating such rankings is to base them on the coauthorship networks in both CiteSeers. In this study, we produce these rankings using 12 different ranking methods including PageRank and its variants, compare them with the lists of ACM A. M. Turing Award and ACM SIGMOD E. F. Codd Innovations Award winners and conclude that the rankings generated from CiteSeer X data outperform those from CiteSeer.

DS-index: Ranking authors distinctively in an Academic network

IEEE Access

The impact and productivity of researchers are assessed using bibliometric parameters, such as the number of publications and citation analysis. A number of indices exist that use these parameters, but almost all of them overlook citation pattern of the researchers, which results in assigning the same index value to two different authors with different citation patterns. In this paper a new index called, DS-index, is proposed which differentiates among the authors having even a very small change in the citation pattern of their publications. It uniquely identifies the different index values and thus the proper ranking order for authors. The index is applied to the self-developed large DBLP data set having publication data of over 50 years. The results compared with the existing indices using the standard performance evaluation measures confirm that the proposed index performs better by ranking the authors in a distinctive order.

Distinctive author ranking using DEA indexing

Quality & Quantity, 2020

The productivity and impact of a researcher can be measured by considering the total number of articles authored by him/her and corresponding citations. Several techniques exist to evaluate the cumulative impact of the author's scholarly output & performance by comparing publications to citations. However, all of them fail to rank each author uniquely, resulting in the same index value assigned to two or more authors, although they have diverse citation patterns. In some indexing, beyond a certain number of citations of a particular article, the subsequent citations do not add any value to the overall indexing. In this paper, a new indexing scheme, based on data envelopment analysis, is proposed which ensures the unique ranking by identifying the different index values of the authors who have even a minimal difference in the citation pattern. Furthermore, the proposed scheme ensures that every citation will have impact without any ceiling. The index is applied to a consistent data set having publications data of the last 40 years in the field of Computer Science. The outcome, when compared with the existing metrics, confirms that the proposed index provides more effective results by ranking authors distinctively.

Ranking authors in digital libraries

Proceeding of the 11th annual international ACM/IEEE joint conference on Digital libraries - JCDL '11, 2011

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.

2015-MURJET Jrl-Mutual Influence based Ranking of Authors.pdf

a challenging task with continuing growth in the size of computationally analysable data and dynamic nature of scholarly networks. This study ranks the authors not only on the basis of their own work but, also measures the impact of mutual influence of authors when they work in collaboration. The work of an author is influenced by the work of his or her co-authors, more in the case if co-authors are senior and less otherwise. This study proposes MuInf Rank (Mutual Influence based Ranking algorithm), which is calculated via a series of experiments. First, we calculate Mutual Influence with respect to number of papers, second, the number of citations and third, we calculate the combined effect of papers and citations with respect to the mutual influence of authors. It is shown empirically that the proposed methods produce satisfying results. have more chances to flourish in future. A researcher who is collaborating with more researchers receives influence by more researchers. Hence, he or she will have high impact of other's work if the collaborators are seniors. Contemplating the impact of an author's own work along with the influence of his or her collaborators give a complete picture of the standing of an author. Existing methods compute the rank of authors based on their in-links information. The proposed method not only involves the in-links information of a node, but also considers out-links of an author. These out-links are determined from the co-authorship relation from a network. This study considers a bibliography network and its essential contributions are finding the

Score‐based bibliometric rankings of authors

Journal of the American Society for Information …, 2009

Scoring rules (or score-based rankings or summation-based rankings) form a family of bibliometric rankings of authors such that authors are ranked according to the sum over all their publications of some partial scores. Many of these rankings are widely used (e.g., number of publications, weighted or not by the impact factor, by the number of authors or by the number of citations,). We present an axiomatic analysis of the family of all scoring rules and of some particular cases within this family.