  Graph neural networks (GNNs) have been applied into a variety of graph tasks.
Most existing work of GNNs is based on the assumption that the given graph data
is optimal, while it is inevitable that there exists missing or incomplete
edges in the graph data for training, leading to degraded performance. In this
paper, we propose Generative Predictive Network (GPN), a GNN-based joint
learning framework that simultaneously learns the graph structure and the
downstream task. Specifically, we develop a bilevel optimization framework for
this joint learning task, in which the upper optimization (generator) and the
lower optimization (predictor) are both instantiated with GNNs. To the best of
our knowledge, our method is the first GNN-based bilevel optimization framework
for resolving this task. Through extensive experiments, our method outperforms
a wide range of baselines using benchmark datasets.
