Bayesian graph convolutional neural networks
AssigneesThe Royal Institution for the Advancement of Learning/Mcgill University, Huawei Technologies Canada Co., Ltd.
InventorsYingxue Zhang, Soumyasundar Pal, Mark Coates, Deniz Ustebay
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Abstract
Method and system for predicting labels for nodes in an observed graph, including deriving a plurality of random graph realizations of the observed graph; learning a predictive function using the random graph realizations; predicting label probabilities for nodes of the random graph realizations using the learned predictive function; and averaging the predicted label probabilities to predict labels for the nodes of the observed graph.
Claims20
A computer implemented method for predicting labels for nodes in an observed graph, comprising:
- deriving a plurality of random graph realizations of the observed graph;
- learning a predictive function using the random graph realizations;
- predicting label probabilities for nodes of the random graph realizations using the learned predictive function;
- averaging the predicted label probabilities to predict labels for the nodes of the observed graph.
A processing unit for predicting labels for nodes in an observed graph, the processing unit comprising a processing device and a storage storing instructions for configuring the processing unit to:
- derive a plurality of random graph realizations of the observed graph;
- learn a predictive function using the random graph realizations;
- predict label probabilities for nodes of the random graph realizations using the learned predictive function;
- average the predicted label probabilities to predict labels for the nodes of the observed graph.
A machine learning system comprising:
- a graph generation module configured to receive as inputs an observed graph and output a plurality of random graph realizations of the observed graph;
- a graph convolution neural network configured to learn a predictive function using the random graph realizations to predict label probabilities for nodes of the random graph realizations, and average the predicted label probabilities to predict labels for the nodes of the observed graph.
Dependencies are inferred from the claim text.