mlsanityData to 7 Oct 2026

Multiple-Source Localization from a Single-Snapshot Observation Using Graph Bayesian Optimization

arXiv 2403.16818 ↗First posted 25 Mar 2024cs.LGcited by 0

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

Due to the significance of its various applications, source localization has garnered considerable attention as one of the most important means to confront diffusion hazards. Multi-source localization from a single-snapshot observation is especially relevant due to its prevalence. However, the inherent complexities of this problem, such as limited information, interactions among sources, and dependence on diffusion models, pose challenges to resolution. Current methods typically utilize heuristics and greedy selection, and they are usually bonded with one diffusion model. Consequently, their effectiveness is constrained. To address these limitations, we propose a simulation-based method termed BOSouL. Bayesian optimization (BO) is adopted to approximate the results for its sample efficiency. A surrogate function models uncertainty from the limited information. It takes sets of nodes as the input instead of individual nodes. BOSouL can incorporate any diffusion model in the data acquisition process through simulations. Empirical studies demonstrate that its performance is robust across graph structures and diffusion models. The code is available at https://github.com/XGraph-Team/BOSouL.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 15 of its 40 references that are in the map. First posted on arXiv 25 Mar 2024.

Builds on

15 of 40 references.

References · 15

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  2. 2018A Tutorial on Bayesian Optimizationcited by 734
  3. 2018GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Accelerationcited by 603
  4. 2019A Survey on Graph Kernelscited by 148
  5. 2014Bayesian Optimization with Unknown Constraintscited by 109
  6. 2015Batch Bayesian Optimization via Local Penalizationcited by 103
  7. 2019Combinatorial Bayesian Optimization using the Graph Cartesian Productcited by 78
  8. 2018GraKeL: A Graph Kernel Library in Pythoncited by 54
  9. 2019Graph Kernels: A Surveycited by 43
  10. 2018Bayesian Semi-supervised Learning with Graph Gaussian Processescited by 28
  11. 2019Graph Convolutional Gaussian Processescited by 20
  12. 2016Catch'Em All: Locating Multiple Diffusion Sources in Networks with Partial Observationscited by 16
3 more
  1. 2021Adaptive Gaussian Processes on Graphs via Spectral Graph Waveletscited by 11
  2. 2020Graph Convolutional Gaussian Processes For Link Predictioncited by 8
  3. 2017NDlib: a Python Library to Model and Analyze Diffusion Processes Over Complex Networkscited by 5

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