  Heterogeneous graphs are ubiquitous to model complex data. There are urgent
needs on powerful heterogeneous graph neural networks to effectively support
important applications. We identify a potential semantic mixing issue in
existing message passing processes, where the representations of the neighbors
of a node $v$ are forced to be transformed to the feature space of $v$ for
aggregation, though the neighbors are in different types. That is, the
semantics in different node types are entangled together into node $v$'s
representation. To address the issue, we propose SlotGAT with separate message
passing processes in slots, one for each node type, to maintain the
representations in their own node-type feature spaces. Moreover, in a
slot-based message passing layer, we design an attention mechanism for
effective slot-wise message aggregation. Further, we develop a slot attention
technique after the last layer of SlotGAT, to learn the importance of different
slots in downstream tasks. Our analysis indicates that the slots in SlotGAT can
preserve different semantics in various feature spaces. The superiority of
SlotGAT is evaluated against 13 baselines on 6 datasets for node classification
and link prediction. Our code is at
https://github.com/scottjiao/SlotGAT_ICML23/.
