Science mapping with asymmetrical paradigmatic proximity
  We propose a series of methods to represent the evolution of a field of science at different levels: namely micro, meso and macro levels. We use a previously introduced asymmetric measure of paradigmatic proximity between terms that enables us to extract structure from a large publications database. We apply our set of methods on a case study from the complex systems community through the mapping of more than 400 complex systems science concepts indexed from a database as large as several millions of journal papers. We will first summarize the main properties of our asymmetric proximity measure. Then we show how salient paradigmatic fields can be embedded into a 2-dimensional visualization into which the terms are plotted according to their relative specificity and generality index. This meso-level helps us producing macroscopic maps of the field of science studied featuring the former paradigmatic fields. 
Jean-Philippe Cointet (CREA, TSV), David Chavalarias (CREA)