  Learning graph convolutional networks (GCNs) is an emerging field which aims
at generalizing convolutional operations to arbitrary non-regular domains. In
particular, GCNs operating on spatial domains show superior performances
compared to spectral ones, however their success is highly dependent on how the
topology of input graphs is defined. In this paper, we introduce a novel
framework for graph convolutional networks that learns the topological
properties of graphs. The design principle of our method is based on the
optimization of a constrained objective function which learns not only the
usual convolutional parameters in GCNs but also a transformation basis that
conveys the most relevant topological relationships in these graphs.
Experiments conducted on the challenging task of skeleton-based action
recognition shows the superiority of the proposed method compared to
handcrafted graph design as well as the related work.
