  The information contained in hierarchical topology, intrinsic to many
networks, is currently underutilised. A novel architecture is explored which
exploits this information through a multiscale decomposition. A dendrogram is
produced by a Girvan-Newman hierarchical clustering algorithm. It is segmented
and fed through graph convolutional layers, allowing the architecture to learn
multiple scale latent space representations of the network, from fine to coarse
grained. The architecture is tested on a benchmark citation network,
demonstrating competitive performance. Given the abundance of hierarchical
networks, possible applications include quantum molecular property prediction,
protein interface prediction and multiscale computational substrates for
partial differential equations.
