  Graph neural networks (GNN) have been ubiquitous in graph learning tasks such
as node classification. 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, but current attention always considers feature similarity and
suffers 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. Its purpose is to reduce
the aggregation's negative disturbance. The soft attention is learned on
features maximizing the information gain by message passing over better graph
structures. Moreover, the learned attention guides the label propagation and
the feature propagation. Extensive experiments are performed on five well-known
benchmark graph datasets to verify the effectiveness of the proposed method.
