Code repository for NeurIPS 2023 paper Bayesian Optimisation of Functions on Graphs.
If you find the paper or the codebase useful to your research, please cite:
@inproceedings{
wan2023bayesian,
title={Bayesian Optimisation of Functions on Graphs},
author={Xingchen Wan and Pierre Osselin and Henry Kenlay and Binxin Ru and Michael A Osborne and Xiaowen Dong},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=UuNd9A6noD}
}
conda create -n graph
conda install networkx numpy pandas matplotlib seaborn jupyterlab
conda install botorch -c pytorch -c gpytorch -c conda-forge
conda activate graph
pip install ndlib
Use the following code block to run a single trial:
from search.run_one_replicate import run_one_replication
save_dir = "./logs/synthetic/"
seed = 0
problem_name = "small_ba_betweenness" # defines the problem
label = "ei_ego_network_polynomial" # defines the method
run_one_replication(
label,
seed=seed,
problem_name=problem_name,
save_path=save_dir,
batch_size=1,
n_initial_points=10,
)To run through command line, define a configuration in the folder config/config.yaml and run the command:
python main.py --config config
For example, to run the centrality experiment with BA graphs, run:
python main.py --config centrality_ba
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