mlsanityData to 7 Oct 2026

Bayesian Optimisation of Functions on Graphs

arXiv 2306.05304 ↗First posted 8 Jun 2023cs.LG cs.AI stat.MLcited by 7

Not on the map. Why?

Abstract (arXiv)

The increasing availability of graph-structured data motivates the task of optimising over functions defined on the node set of graphs. Traditional graph search algorithms can be applied in this case, but they may be sample-inefficient and do not make use of information about the function values; on the other hand, Bayesian optimisation is a class of promising black-box solvers with superior sample efficiency, but it has been scarcely been applied to such novel setups. To fill this gap, we propose a novel Bayesian optimisation framework that optimises over functions defined on generic, large-scale and potentially unknown graphs. Through the learning of suitable kernels on graphs, our framework has the advantage of adapting to the behaviour of the target function. The local modelling approach further guarantees the efficiency of our method. Extensive experiments on both synthetic and real-world graphs demonstrate the effectiveness of the proposed optimisation framework.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 22 of its 48 references that are in the map. First posted on arXiv 8 Jun 2023.

Builds on

22 of 48 references.

References · 22

  1. 2017Inductive Representation Learning on Large Graphscited by 6,284
  2. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  3. 2018A Tutorial on Bayesian Optimizationcited by 734
  4. 2019Multi-scale Attributed Node Embeddingcited by 543
  5. 2019Scalable Global Optimization via Local Bayesian Optimizationcited by 359
  6. 2018Neural Architecture Search with Bayesian Optimisation and Optimal Transportcited by 192
  7. 2021Multi-Objective Bayesian Optimization over High-Dimensional Search Spacescited by 87
  8. 2019Combinatorial Bayesian Optimization using the Graph Cartesian Productcited by 78
  9. 2019ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendationscited by 77
  10. 2020Mat'ern Gaussian Processes on Graphscited by 65
  11. 2018GEMSEC: Graph Embedding with Self Clusteringcited by 61
  12. 2021Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spacescited by 51
10 more
  1. 2018Bayesian Semi-supervised Learning with Graph Gaussian Processescited by 28
  2. 2020Mercer Features for Efficient Combinatorial Bayesian Optimizationcited by 27
  3. 2020Gaussian Processes on Graphs via Spectral Kernel Learningcited by 24
  4. 2019Graph Convolutional Gaussian Processescited by 20
  5. 2022Bayesian Generational Population-Based Trainingcited by 20
  6. 2018Gaussian Processes Over Graphscited by 14
  7. 2021Adaptive Gaussian Processes on Graphs via Spectral Graph Waveletscited by 11
  8. 2020Graph Convolutional Gaussian Processes For Link Predictioncited by 8
  9. 2021Maximizing and Satisficing in Multi-armed Bandits with Graph Informationcited by 5
  10. 2019Deep Bayesian Optimization on Attributed Graphscited by 2

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 5.

7 papers.

  1. 2023Adaptive Batch Sizes for Active Learning A Probabilistic Numerics Approachcited by 6
  2. 2025Graph Random Features for Scalable Gaussian Processescited by 4
  3. 2025Global optimization of graph acquisition functions for neural architecture searchcited by 2
  4. 2024Bayesian Optimization of Functions over Node Subsets in Graphscited by 1
  5. 2025Conformalized Gaussian processes for online uncertainty quantification over graphscited by 1
  6. 2025Omnipresent Yet Overlooked: Heat Kernels in Combinatorial Bayesian Optimizationcited by 1
  7. 2025Global Optimization on Graph-Structured Data via Gaussian Processes with Spectral Representationscited by 0