Neural message passing on high order paths

Graph neural networks have achieved impressive results in predicting molecular properties, but they do not directly account for local and hidden structures in the graph such as functional groups and molecular geometry. At each propagation step, graph neural networks aggregate only over first order neighbours and can only learn about important information contained in subsequent neighbours as well as the relationships between those higher order connections—over many propagation steps. In this work, we generalize graph neural nets to pass messages and aggregate across higher order paths. This allows for information to propagate over various levels and substructures of the graph. We demonstrate our model on a few tasks in molecular property prediction.