2013/01/30 by David F. Klosik, Stefan Bornholdt
Computer Science · Decision Sciences · Physics and Astronomy · #Biology #Citation #Citation analysis #Complex Network Analysis Techniques #Computer science #Data Visualization and Analytics #Data science #Information retrieval #Scientific literature #Set (abstract data type) #World Wide Web #cs.DL #physics.soc-ph #scientometrics and bibliometrics research
paper · pdf · doi:10.1371/journal.pone.0113184
11 pages, 3 figures
arxiv created 2013/01/30 · openalex publication_date 2014/12/01 · arxiv updated 2015/06/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
For several decades, a leading paradigm of how to quantitatively assess scientific research has been the analysis of the aggregated citation information in a set of scientific publications. Although the representation of this information as a citation network has already been coined in the 1960s, it needed the systematic indexing of scientific literature to allow for impact metrics that actually made use of this network as a whole, improving on the then prevailing metrics that were almost exclusively based on the number of direct citations. However, besides focusing on the assignment of credit, the paper citation network can also be studied in terms of the proliferation of scientific ideas. Here we introduce a simple measure based on the shortest-paths in the paper's in-component or, simply speaking, on the shape and size of the wake of a paper within the citation network. Applied to a citation network containing Physical Review publications from more than a century, our approach is able to detect seminal articles which have introduced concepts of obvious importance to the further development of physics. We observe a large fraction of papers co-authored by Nobel Prize laureates in physics among the top-ranked publications.