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.
An probabilistic inference algorithm driven by neural network for graph-structured models This paper introduces policy message passing, a graph neural network with an inference mechanism that assigns messages to edges in a recurrent fashion, indicating competitive performance on visual reasoning tasks.