HeterSEED decouples semantics from structure in heterogeneous graphs under heterophily using separate channels and adaptive fusion, proving higher expressiveness and lower bias than standard HGNNs while outperforming baselines on large graphs.
Towards learning from graphs with heterophily: Progress and future.Frontiers of Computer Science, 20: 2002314
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HeterSEED: Semantics-Structure Decoupling for Heterogeneous Graph Learning under Heterophily
HeterSEED decouples semantics from structure in heterogeneous graphs under heterophily using separate channels and adaptive fusion, proving higher expressiveness and lower bias than standard HGNNs while outperforming baselines on large graphs.