  Graph Neural Networks (GNNs) have received much attention recent years and
have achieved state-of-the-art performances in many fields. The deeper GNNs can
theoretically capture deeper neighborhood information. However, they often
suffer from problems of over-fitting and over-smoothing. In order to
incorporate deeper information while preserving considerable complexity and
generalization ability, we propose Adaptive Graph Diffusion Networks with
Hop-wise Attention (AGDNs-HA). We stack multi-hop neighborhood aggregations of
different orders into single layer. Then we integrate them with the help of
hop-wise attention, which is learnable and adaptive for each node. Experimental
results on the standard dataset with semi-supervised node classification task
show that our proposed methods achieve significant improvements.
