  Graph convolutional networks (GCNs) have achieved huge success in several
machine learning (ML) tasks on graph-structured data. Recently, several
sampling techniques have been proposed for the efficient training of GCNs and
to improve the performance of GCNs on ML tasks. Specifically, the
subgraph-based sampling approaches such as ClusterGCN and GraphSAINT have
achieved state-of-the-art performance on the node classification tasks. These
subgraph-based sampling approaches rely on heuristics -- such as graph
partitioning via edge cuts -- to identify clusters that are then treated as
minibatches during GCN training. In this work, we hypothesize that rather than
relying on such heuristics, one can learn a reinforcement learning (RL) policy
to compute efficient clusters that lead to effective GCN performance. To that
end, we propose PolicyClusterGCN, an online RL framework that can identify good
clusters for GCN training. We develop a novel Markov Decision Process (MDP)
formulation that allows the policy network to predict ``importance" weights on
the edges which are then utilized by a clustering algorithm (Graclus) to
compute the clusters. We train the policy network using a standard policy
gradient algorithm where the rewards are computed from the classification
accuracies while training GCN using clusters given by the policy. Experiments
on six real-world datasets and several synthetic datasets show that
PolicyClusterGCN outperforms existing state-of-the-art models on node
classification task.
