Enhanced Epidemic Control: Community-Based Observer Placement and Source Tracing
提出一种基于社区的源定位模型，通过将网络划分为社区并优化观察者部署，提高大规模网络中识别扩散源头的效率和准确性。
Identifying the diffusion origin within networks is critically important for controlling the spread of information, diseases, or other contagions. In this work, we study how to recognize the diffusion source based on limited observational knowledge. To date, the applicability of existing methods is often challenged when dealing with large-scale networks. To improve the scalability, we here develop a community-based source localization (CSL) model by splitting the network into several clusters from the community perspective. In addition, we study an issue that has received less attention but is rather important, i.e., observer deployment. Specifically, we categorize three types of observers and propose a two-stage placement strategy to enhance the accuracy of localization. Our findings suggest that the optimized deployed observers tend to situate at key positions and have higher betweenness compared to non-observer nodes. Based on the information obtained from deployed observers, we further develop five different strategies to locate the community where the source belongs, and our study shows that the diffusion often occurs within a small number of communities so that the search scope can be effectively refined. We validate our method through simulations on various networks, and the experimental results demonstrate the excellent efficacy and efficiency of CSL in early source localization.