HA-HeteroGNN uses two-tier attention to produce relevance scores for pruning nodes in heterogeneous graphs, cutting edges by 27% while raising classification accuracy by 2.4-6.1% on a 50,000-record synthetic dataset.
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Hierarchical Attention-based Graph Neural Network with Relevance-driven Pruning
HA-HeteroGNN uses two-tier attention to produce relevance scores for pruning nodes in heterogeneous graphs, cutting edges by 27% while raising classification accuracy by 2.4-6.1% on a 50,000-record synthetic dataset.