Related papers: Neural Message Passing on High Order Paths

Neural Message Passing on High Order Paths

URL: http://arxiv.org/abs/2002.10413v1
Date: Mon, 24 Feb 2020 17:58:02 GMT
Title: Neural Message Passing on High Order Paths
Authors: Daniel Flam-Shepherd, Tony Wu, Pascal Friederich and Alan Aspuru-Guzik
Abstract summary: 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.
Score: 4.273470365293033
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Graph neural network 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, GNNs aggregate only over first order neighbours, ignoring important information contained in subsequent neighbours as well as the relationships between those higher order connections. 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.

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