  Investigating graph feature learning becomes essentially important with the
emergence of graph data in many real-world applications. Several graph neural
network approaches are proposed for node feature learning and they generally
follow a neighboring information aggregation scheme to learn node features.
While great performance has been achieved, the weights learning for different
neighboring nodes is still less explored. In this work, we propose a novel
graph network layer, known as Node2Seq, to learn node embeddings with
explicitly trainable weights for different neighboring nodes. For a target
node, our method sorts its neighboring nodes via attention mechanism and then
employs 1D convolutional neural networks (CNNs) to enable explicit weights for
information aggregation. In addition, we propose to incorporate non-local
information for feature learning in an adaptive manner based on the attention
scores. Experimental results demonstrate the effectiveness of our proposed
Node2Seq layer and show that the proposed adaptively non-local information
learning can improve the performance of feature learning.
