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.
On the cross-type homophily of heterogeneous graphs: Understanding and unleashing
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.