Ranking Scientific Publications Based on Their Citation Graph

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.

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.

Journal of Statistical …, 2007

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.

This paper deals with the definitions, explanations and testing of the PageRank formula modified and adapted for bibliographic networks. Our modifications of PageRank take into account not only the citations but also the co-authorship relationships. We verified the capabilities of the developed algorithms by applying them to the data from the DBLP digital library and subsequently by comparing the resulting ranks of the sixteen winners of the ACM SIGMOD E.F.Codd Innovations Award from the years 1992 till 2007. Such ranking, which is based on both the citation and co-authorship information, gives better and more fairminded results than the standard PageRank gives. The proposed method is able to reduce the influence of citation loops and gives the opportunity for farther improvements e.g. introducing temporal views into the citations evaluating algorithms.

arXiv preprint arXiv:1207.6328, 2012

Abstract: Bibliometric indexes are customary used in evaluating the impact of scientific research, even though it is very well known that in different research areas they may range in very different intervals. Sometimes, this is evident even within a single given field of investigation making very difficult (and inaccurate) the assessment of scientific papers. On the other hand, the problem can be recast in the same framework which has allowed to efficiently cope with the ordering of web-pages, ie, to formulate the PageRank of Google. ...

The Journal of Neuroscience, 2008

2015

We dene a modied PageRank algorithm and the P R-score to measure the inuence of a single article by using its local co-citation network. We also calculate the reaching probability and RP-score of a paper starting at an arbitrary article of its co-citation network for the same purpose. We highlight the advantages of our methods by applying them on the celebrated paper of Jen® Egerváry that is underrated by the standard indices.

2011 IEEE 11th International Conference on Data Mining Workshops, 2011

Many algorithms have been developed to identify important nodes in a complex network, including various centrality metrics and PageRank, but most fail to consider the dynamic nature of the network. They therefore suffer from recency bias and fail to recognize important new nodes that have not had as much time to accumulate links as their older counterparts. This paper describes the Effective Contagion Matrix (ECM), a solution to address recency bias in the analysis of dynamic complex networks. The idea of ECM is to explicitly consider the temporal order of links and chains of links connecting to a node with some temporal decay factors. We tested ECM with three large real world citation networks on the task of predicting papers' future importance. We compared ECM's performance with two static metrics, degree-centrality and PageRank, and two time-aware metrics, age-based PageRank and CiteRank. We show that ECM is more appropriate for predicting future citations and PageRank scores with regard to new citations. We also describe a procedure to estimate ECM's parameters from the data. Combining all five scores into a ν-SVR regression model of future citations improves the predictive performance further.

Scientific Reports, 2014