  A general graph-structured neural network architecture operates on graphs
through two core components: (1) complex enough message functions; (2) a fixed
information aggregation process. In this paper, we present the Policy Message
Passing algorithm, which takes a probabilistic perspective and reformulates the
whole information aggregation as stochastic sequential processes. The algorithm
works on a much larger search space, utilizes reasoning history to perform
inference, and is robust to noisy edges. We apply our algorithm to multiple
complex graph reasoning and prediction tasks and show that our algorithm
consistently outperforms state-of-the-art graph-structured models by a
significant margin.
