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

• URL: http://arxiv.org/abs/2403.16818v1
• Date: Mon, 25 Mar 2024 14:46:24 GMT
• Title: Multiple-Source Localization from a Single-Snapshot Observation Using Graph Bayesian Optimization
• Authors: Zonghan Zhang, Zijian Zhang, Zhiqian Chen,
• Abstract summary: Multi-source localization from a single snap-shot observation is especially relevant due to its prevalence. Current methods typically utilizes and greedy selection, and they are usually bonded with one diffusion model. We propose a simulation-based method termed BOSouL to approximate the results for its sample efficiency.
• Score: 10.011338977476804
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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