  Graph convolutional networks (GCNs) aim at extending deep learning to
arbitrary irregular domains, namely graphs. Their success is highly dependent
on how the topology of input graphs is defined and most of the existing GCN
architectures rely on predefined or handcrafted graph structures. In this
paper, we introduce a novel method that learns the topology (or connectivity)
of input graphs as a part of GCN design. The main contribution of our method
resides in building an orthogonal connectivity basis that optimally aggregates
nodes, through their neighborhood, prior to achieve convolution. Our method
also considers a stochasticity criterion which acts as a regularizer that makes
the learned basis and the underlying GCNs lightweight while still being highly
effective. Experiments conducted on the challenging task of skeleton-based
hand-gesture recognition show the high effectiveness of the learned GCNs w.r.t.
the related work.
