Scalable and Parallel Deep Bayesian Optimization on Attributed Graphs

Jiaxu Cui, Bo Yang*, Bingyi Sun, Xia Hu, Jiming Liu

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review


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.

Original languageEnglish
Pages (from-to)103-116
Number of pages14
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number1
Early online date13 Oct 2020
Publication statusPublished - Jan 2022

Scopus Subject Areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

User-Defined Keywords

  • Attributed graphs
  • Bayesian optimization
  • graph neural networks (GNNs)
  • structure optimization.


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