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.

Abstract:In the past, recursive algorithms, such as PageRank originally conceived for the Web, have been successfully used to rank nodes in the citation networks of papers, authors, or journals. They have proved to determine prestige and not popularity, unlike citation counts.However, bibliographic networks, in contrast to the Web, have some specific features that enable the assigning of different weights to citations, thus adding more information to the process of finding prominence. For example, a citation between two authors may be weighed according to whether and when those two authors collaborated with each other, which is information that can be found in the co-authorship network. In this study, we define a couple of PageRank modifications that weigh citations between authors differently based on the information from the co-authorship graph. In addition, we put emphasis on the time of publications and citations. We test our algorithms on the Web of Science data of computer

Abstract:The CiteSeer digital library is a useful source of bibliographic information. It allows for retrieving citations, co-authorships, addresses, and affiliations of authors and publications. In spite of this, it has been relatively rarely used for automated citation analyses. This article describes our findings after extensively mining from the CiteSeer data. We explored citations between authors and determined rankings of influential scientists using various evaluation methods including citation and in-degree counts, HITS, PageRank, and its variations based on both the citation and collaboration graphs. We compare the resulting rankings with lists of computer science award winners and find out that award recipients are almost always ranked high. We conclude that CiteSeer is a valuable, yet not fully appreciated, repository of citation data and is appropriate for testing novel bibliometric methods.

Abstract:In this article we present a bibliometric study of 1.9 million computer science papers published from 1945 to 2014 and indexed in Web of Science. We analyze both the quantity and the impact of these publications according to document types, languages, disciplines, countries, institutions, and publication sources. The most frequent author keywords, cited references, and cited papers as well as the distribution of the number of references and citations per paper and of the age of cited references are also explored. Since conference proceedings play a tremendous role in this scientific field, we investigate the time and place of computer science conferences in terms of the most prolific months and locations. And, last but not least, the production of journal articles and conference papers over the whole time period and the level of collaboration in different computer science disciplines are inspected. One of the main results is the finding that "Artificial Intelligence" is the most productive subfield of computer science, but "Interdisciplinary Applications" has the highest relative impact.