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/.

翻译：异构图在建模复杂数据中无处不在，迫切需要强大的异构图神经网络来有效支持重要应用。我们发现在现有消息传递过程中存在潜在的语义混合问题：当节点$v$的邻居节点类型不同时，这些邻居表示被强制转换到节点$v$的特征空间进行聚合，导致不同节点类型的语义被纠缠并混入节点$v$的表示中。为解决该问题，我们提出SlotGAT，通过在每个槽中维持独立的消息传递过程（每个节点类型对应一个槽），使表示保留在其所属节点类型的特征空间中。此外，在基于槽的消息传递层中，我们设计了注意力机制以实现有效的逐槽消息聚合。进一步，我们在SlotGAT最后一层后开发了槽注意力技术，用于学习下游任务中不同槽的重要性。分析表明，SlotGAT中的槽能够在不同特征空间中保留不同语义。我们在6个数据集上针对节点分类和链接预测任务，将本文方法与13个基线模型进行了对比评估。代码开源地址：https://github.com/scottjiao/SlotGAT_ICML23/。