  Graph neural networks (GNN) have been ubiquitous in graph node classification
tasks. Most of GNN methods update the node embedding iteratively by aggregating
its neighbors' information. However, they often suffer from negative
disturbance, due to edges connecting nodes with different labels. One approach
to alleviate this negative disturbance is to use attention to learn the weights
of aggregation, but current attention-based GNNs only consider feature
similarity and also suffer from the lack of supervision. In this paper, we
consider the label dependency of graph nodes and propose a decoupling attention
mechanism to learn both hard and soft attention. The hard attention is learned
on labels for a refined graph structure with fewer inter-class edges, so that
the aggregation's negative disturbance can be reduced. The soft attention aims
to learn the aggregation weights based on features over the refined graph
structure to enhance information gains during message passing. Particularly, we
formulate our model under the EM framework, and the learned attention is used
to guide the label propagation in the M-step and the feature propagation in the
E-step, respectively. Extensive experiments are performed on six well-known
benchmark graph datasets to verify the effectiveness of the proposed method.
