Adaptive Graph Diffusion Networks with Hop-wise Attention
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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.
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