GC splits a graph supernet into sub-supernets by grouping modules with similar gradient contributions, and UGAS searches combined MPNN and graph transformer architectures; the searched GNNs beat several baselines.
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Towards Efficient Few-shot Graph Neural Architecture Search via Partitioning Gradient Contribution
GC splits a graph supernet into sub-supernets by grouping modules with similar gradient contributions, and UGAS searches combined MPNN and graph transformer architectures; the searched GNNs beat several baselines.