Multiple-Source Localization from a Single-Snapshot Observation Using
  Graph Bayesian Optimization
Zonghan Zhang Zijian Zhang Zhiqian Chen
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.