  Graph Neural Architecture Search (GNAS) has achieved superior performance on
various graph-structured tasks. However, existing GNAS studies overlook the
applications of GNAS in resource-constraint scenarios. This paper proposes to
design a joint graph data and architecture mechanism, which identifies
important sub-architectures via the valuable graph data. To search for optimal
lightweight Graph Neural Networks (GNNs), we propose a Lightweight Graph Neural
Architecture Search with Graph SparsIfication and Network Pruning (GASSIP)
method. In particular, GASSIP comprises an operation-pruned architecture search
module to enable efficient lightweight GNN search. Meanwhile, we design a novel
curriculum graph data sparsification module with an architecture-aware
edge-removing difficulty measurement to help select optimal sub-architectures.
With the aid of two differentiable masks, we iteratively optimize these two
modules to efficiently search for the optimal lightweight architecture.
Extensive experiments on five benchmarks demonstrate the effectiveness of
GASSIP. Particularly, our method achieves on-par or even higher node
classification performance with half or fewer model parameters of searched GNNs
and a sparser graph.
