  The present work investigates clustering of a graph-based representation of
industrial connections derived from international trade data by Hidalgo et al
(2007) and confirms existence of around ten industrial clusters that are
reasonably consistent with expected historical patterns of diffusion of
innovation and technology. This supports the notion that technological
development occurs in sequential innovation waves. The clustering method
developed in this work follows conceptual ideas of Lambiotte and Barahona
(2009), who suggested to use random walk to assess a hierarchical structure of
network communities where different levels of the hierarchy correspond to
different diffusion times. We, however, implement these ideas differently to
match physics of the problem under consideration and introduce a hierarchal
clustering procedure that is combined with convenient resorting of the
elements. An equivalent spectral interpretation of the clustering is also given
and discussed in the paper.
