  Graph convolution networks (GCNs) are currently mainstream in learning with
irregular data. These models rely on message passing and attention mechanisms
that capture context and node-to-node relationships. With multi-head attention,
GCNs become highly accurate but oversized, and their deployment on cheap
devices requires their pruning. However, pruning at high regimes usually leads
to topologically inconsistent networks with weak generalization. In this paper,
we devise a novel method for lightweight GCN design. Our proposed approach
parses and selects subnetworks with the highest magnitudes while guaranteeing
their topological consistency. The latter is obtained by selecting only
accessible and co-accessible connections which actually contribute in the
evaluation of the selected subnetworks. Experiments conducted on the
challenging FPHA dataset show the substantial gain of our topologically
consistent pruning method especially at very high pruning regimes.
