  We present a novel and hierarchical approach for supervised classification of
signals spanning over a fixed graph, reflecting shared properties of the
dataset. To this end, we introduce a Convolutional Cluster Pooling layer
exploiting a multi-scale clustering in order to highlight, at different
resolutions, locally connected regions on the input graph. Our proposal
generalises well-established neural models such as Convolutional Neural
Networks (CNNs) on irregular and complex domains, by means of the exploitation
of the weight sharing property in a graph-oriented architecture. In this work,
such property is based on the centrality of each vertex within its
soft-assigned cluster. Extensive experiments on NTU RGB+D, CIFAR-10 and 20NEWS
demonstrate the effectiveness of the proposed technique in capturing both local
and global patterns in graph-structured data out of different domains.
