PageRank for bibliographic networks

descriptionPublicationkeyboard_double_arrow_right Article 22 May 2008 English Publisher:Springer Science and Business Media LLCJournal:Scientometrics, volume 76, pages 135-158 (issn: 0138-9130, eissn: 1588-2861,

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Authors: Dalibor Fiala; François Rousselot; Karel Jezek;

doi: 10.1007/s11192-007-1908-4

PageRank for bibliographic networks

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Abstract

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

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Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	59
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%