A novel approach to Rank Authors in an Academic Network

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.

Scientometrics, 2008

In this paper, we present several modifications of the classical PageRank formula adapted for bibliographic networks. Our versions of PageRank take into account not only the citation but also the co-authorship graph. We verify the viability of our algorithms by applying them to the data from the DBLP digital library and by comparing the resulting ranks of the winners of the ACM E. F. Codd Innovations Award. Rankings based on both the citation and co-authorship information turn out to be "better" than the standard PageRank ranking.

2011

In this paper we propose an efficient method to rank the research papers from various fields of research published in various conferences over the years. This ranking method is based on citation network. The importance of a research paper is captured well by the peer vote, which in this case is the research paper being cited in other research papers. Using a modified version of the PageRank algorithm, we rank the research papers, assigning each of them an authoritative score. Using the scores of the research papers calculated by above mentioned method, we formulate scores for conferences and authors and rank them as well. We have introduced a new metric in the algorithm which takes into account the time factor in ranking the research papers to reduce the bias against the recent papers which get less time for being studied and consequently cited by the researchers as compared to the older papers. Often a researcher is more interested in finding the top conferences in a particular year rather than the overall conference ranking. Considering the year of publication of the papers, in addition to the paper scores we also calculated the year-wise score of each conference by slight improvisation of the above mentioned algorithm.

Procedia Computer Science, 2014

The indices currently used by scholarly databases, such as Google scholar, to rank scientists, do not attach weights to the citations. Neither is the underlying network structure of citations considered in computing these metrics. This results in scientists cited by well-recognized journals not being rewarded, and may lead to potential misuse if documents are created purely to cite others. In this paper we introduce a new ranking metric, the p-index (pagerank-index), which is computed from the underlying citation network of papers, and uses the pagerank algorithm in its computation. The index is a percentile score, and can potentially be implemented in public databases such as Google scholar, and can be applied at many levels of abstraction. We demonstrate that the metric aids in fairer ranking of scientists compared to h-index and its variants. We do this by simulating a realistic model of the evolution of citation and collaboration networks in a particular field, and comparing h-index and p-index of scientists under a number of scenarios. Our results show that the p-index is immune to author behaviors that can result in artificially bloated h-index values.

2011

This article provides an alternative perspective for measuring author impact by applying PageRank algorithm to a coauthorship network. A weighted PageRank algorithm considering citation and coauthorship network topology is proposed. We test this algorithm under different damping factors by evaluating author impact in the informetrics research community.

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

res publication, 2012

In this paper we propose an efficient method to rank the research papers from various fields of research published in various conferences over the years. This ranking method is based on citation network. The importance of a research paper is captured well by the peer vote, which in this case is the research paper being cited in other research papers. Using a modified version of the PageRank algorithm, we rank the research papers, assigning each of them an authoritative score. Using the scores of the research papers calculated by above mentioned method, we formulate scores for conferences and authors and rank them as well. We have introduced a new metric in the algorithm which takes into account the time factor in ranking the research papers to reduce the bias against the recent papers which get less time for being studied and consequently cited by the researchers as compared to the older papers. Often a researcher is more interested in finding the top conferences in a particular year rather than the overall conference ranking. Considering the year of publication of the papers, in addition to the paper scores we also calculated the year wise score of each conference by slight improvisation of the above mentioned algorithm.

Abstract. As the wealth of data on the World Wide Web grows, and as the structuring of that data improves, more sophisticated applications can be developed to derive meaningful characteristics relating to the content and structure of that data. In particular, ranking the various elements of sets of structured information is of great utility with respect to semantic network analysis.

Seventh IEEE International Conference on Data Mining (ICDM 2007), 2007

The problem of evaluating scientific publications and their authors is important, and as such has attracted increasing attention. Recent graph-theoretic ranking approaches have demonstrated remarkable successes, but most of their applications are limited to homogeneous networks such as the network of citations between publications. This paper proposes a novel method for co-ranking authors and their publications using several networks: the social network connecting the authors, the citation network connecting the publications, as well as the authorship network that ties the previous two together. The new coranking framework is based on coupling two random walks, that separately rank authors and documents following the PageRank paradigm. As a result, improved rankings of documents and their authors depend on each other in a mutually reinforcing way, thus taking advantage of the additional information implicit in the heterogeneous network of authors and documents. The proposed ranking approach has been tested using data collected from CiteSeer, and demonstrates a great improvement in author ranking quality compared with ranking by the number of publications, the number of citations and the PageRank calculated in the authors' social network. This paper proposes a framework for co-ranking entities of different kinds in a heterogeneous network connect

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.