  Graph representation learning, aiming to learn low-dimensional
representations which capture the geometric dependencies between nodes in the
original graph, has gained increasing popularity in a variety of graph analysis
tasks, including node classification and link prediction. Existing
representation learning methods based on graph neural networks and their
variants rely on the aggregation of neighborhood information, which makes it
sensitive to noises in the graph. In this paper, we propose Graph Denoising
Policy Network (short for GDPNet) to learn robust representations from noisy
graph data through reinforcement learning. GDPNet first selects signal
neighborhoods for each node, and then aggregates the information from the
selected neighborhoods to learn node representations for the down-stream tasks.
Specifically, in the signal neighborhood selection phase, GDPNet optimizes the
neighborhood for each target node by formulating the process of removing noisy
neighborhoods as a Markov decision process and learning a policy with
task-specific rewards received from the representation learning phase. In the
representation learning phase, GDPNet aggregates features from signal neighbors
to generate node representations for down-stream tasks, and provides
task-specific rewards to the signal neighbor selection phase. These two phases
are jointly trained to select optimal sets of neighbors for target nodes with
maximum cumulative task-specific rewards, and to learn robust representations
for nodes. Experimental results on node classification task demonstrate the
effectiveness of GDNet, outperforming the state-of-the-art graph representation
learning methods on several well-studied datasets. Additionally, GDPNet is
mathematically equivalent to solving the submodular maximizing problem, which
theoretically guarantees the best approximation to the optimal solution with
GDPNet.
