  The article presents three advanced citation-based methods used to detect
potential breakthrough papers among very highly cited papers. We approach the
detection of such papers from three different perspectives in order to provide
different typologies of breakthrough papers. In all three cases we use the
classification of scientific publications developed at CWTS based on direct
citation relationships. This classification establishes clusters of papers at
three levels of aggregation. Papers are clustered based on their similar
citation orientations and it is assumed that they are focused on similar
research interests. We use the clustering as the context for detecting
potential breakthrough papers. We utilize the Characteristics Scores and Scales
(CSS) approach to partition citation distributions and implement a specific
filtering algorithm to sort out potential highly-cited followers, papers not
considered breakthroughs in themselves. After invoking thresholds and
filtering, three methods are explored: A very exclusive one where only the
highest cited paper in a micro-cluster is considered as a potential
breakthrough paper (M1); as well as two conceptually different methods, one
that detects potential breakthrough papers among the two percent highest cited
papers according to CSS (M2a), and finally a more restrictive version where, in
addition to the CSS two percent filter, knowledge diffusion is also taken in as
an extra parameter (M2b). The advance citation-based methods are explored and
evaluated using specifically validated publication sets linked to different
Danish funding instruments including centres of excellence.
