  This study concentrates on extracting painting art history knowledge from the
network structure of Wikipedia. Therefore, we construct theoretical networks of
webpages representing the hyper-linked structure of articles of 7 Wikipedia
language editions. These 7 networks are analyzed to extract the most
influential painters in each edition using Google matrix theory. Importance of
webpages of over 3000 painters are measured using PageRank algorithm. The most
influential painters are enlisted and their ties are studied with the reduced
Google matrix analysis. Reduced Google Matrix is a powerful method that
captures both direct and hidden interactions between a subset of selected nodes
taking into account the indirect links between these nodes via the remaining
part of large global network. This method originates from the scattering theory
of nuclear and mesoscopic physics and field of quantum chaos. From this study,
we show that it is possible to extract from the components of the reduced
Google matrix meaningful information on the ties between these painters. For
instance, our analysis groups together painters that belong to the same
painting movement and shows meaningful ties between painters of different
movements. We also determine the influence of painters on world countries using
link sensitivity between Wikipedia articles of painters and countries. The
reduced Google matrix approach allows to obtain a balanced view of various
cultural opinions of Wikipedia language editions. The world countries with the
largest number of top painters of selected 7 Wikipedia editions are found to be
Italy, France, Russia. We argue that this approach gives meaningful information
about art and that it could be a part of extensive network analysis on human
knowledge and cultures.
