Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks

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 nodeclassification experiments, we demonstrate the effectiveness of our approach. Graph neural networks (GNNs) have become the de facto standard for representation learning on relational data (Bronstein et al., 2017; Gilmer et al., 2017; Battaglia et al., 2018).