  We propose a dynamic neighborhood aggregation (DNA) procedure guided by
(multi-head) attention for representation learning on graphs. In contrast to
current graph neural networks which follow a simple neighborhood aggregation
scheme, our DNA procedure allows for a selective and node-adaptive aggregation
of neighboring embeddings of potentially differing locality. In order to avoid
overfitting, we propose to control the channel-wise connections between input
and output by making use of grouped linear projections. In a number of
transductive node-classification experiments, we demonstrate the effectiveness
of our approach.
