  In recent years, a new interest for the use of graph-theory based networks
has emerged within the field of cognitive science. This has played a key role
in mining the large amount of data generated by word association norms. In the
present work, we applied semantic network analyses to explore norms of French
word associations for concrete and abstract concepts (Lakhzoum et al., 2021).
Graph analyses have shown that the network exhibits high clustering
coefficient, sparse density, and small average shortest path length for both
the concrete and abstract networks. These characteristics are consistent with a
small-world structure. Comparisons between local node statistics and global
structural topology showed that abstract and concrete concepts present a
similar local connectivity but different overall patterns of structural
organisation with concrete concepts presenting an organisation in densely
connected communities compared to abstract concepts. These patterns confirm
previously acquired knowledge about the dichotomy of abstract and concrete
concepts on a larger scale. To the best of our knowledge, this is the first
attempt to confirm the generalisability of these properties to the French
language and with an emphasis on abstract and concrete concepts.
