Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks. (arXiv:2206.11081v1 [cs.LG])

Heterogeneous graph neural networks (GNNs) achieve strong performance on node
classification tasks in a semi-supervised learning setting. However, as in the
simpler homogeneous GNN case, message-passing-based heterogeneous GNNs may
struggle to balance between resisting the oversmoothing occuring in deep models
and capturing long-range dependencies graph structured data. Moreover, the
complexity of this trade-off is compounded in the heterogeneous graph case due
to the disparate heterophily relationships between nodes of different types. To
address these issues, we proposed a novel heterogeneous …

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