Replacing the hand-tuned global q of magnetic digraph GNNs with an automatically computed per-edge q improves accuracy on 12 directed-graph benchmarks, with a trainable variant reaching reported SOTA.
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Toward Effective Digraph Representation Learning: A Magnetic Adaptive Propagation based Approach
Replacing the hand-tuned global q of magnetic digraph GNNs with an automatically computed per-edge q improves accuracy on 12 directed-graph benchmarks, with a trainable variant reaching reported SOTA.