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Scalable and Parallel Deep Bayesian Optimization on Attributed Graphs
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DOI：10.1109/TNNLS.2020.3027552.png)
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Abstract
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We propose a general and scalable global optimization framework directly operating on annotated graph data by introducing a Bayesian graph neural network to approximate the expensive-to-evaluate objectives. It prevents the cubical complexity of Gaussian processes and can scale linearly with the number of observations. Its parallelized variant makes it scalable. We provide strict theoretical support on its convergence. Intensive experiments conducted on both artificial and real-world problems, including molecular discovery and urban road network design, demonstrate the effectiveness of the proposed methods compared with the current state of the art.
Keywords:
Optimization
Bayes methods
Probabilistic logic
Task analysis
Roads
Convergence
Attributed graphs
Bayesian optimization
graph neural networks (GNNs)
structure optimization
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