  Graph data are pervasive in many real-world applications. Recently,
increasing attention has been paid on graph neural networks (GNNs), which aim
to model the local graph structures and capture the hierarchical patterns by
aggregating the information from neighbors with stackable network modules.
Motivated by the observation that different nodes often require different
iterations of aggregation to fully capture the structural information, in this
paper, we propose to explicitly sample diverse iterations of aggregation for
different nodes to boost the performance of GNNs. It is a challenging task to
develop an effective aggregation strategy for each node, given complex graphs
and sparse features. Moreover, it is not straightforward to derive an efficient
algorithm since we need to feed the sampled nodes into different number of
network layers. To address the above challenges, we propose Policy-GNN, a
meta-policy framework that models the sampling procedure and message passing of
GNNs into a combined learning process. Specifically, Policy-GNN uses a
meta-policy to adaptively determine the number of aggregations for each node.
The meta-policy is trained with deep reinforcement learning (RL) by exploiting
the feedback from the model. We further introduce parameter sharing and a
buffer mechanism to boost the training efficiency. Experimental results on
three real-world benchmark datasets suggest that Policy-GNN significantly
outperforms the state-of-the-art alternatives, showing the promise in
aggregation optimization for GNNs.
