Diffusion Source Identification on Networks with Statistical Confidence
This addresses the problem of identifying sources in diffusion processes, such as rumors or viruses, for applications like rumor controlling and virus identification, providing a method with theoretical guarantees on general networks.
The paper tackles diffusion source identification on networks by introducing a statistical framework with a confidence set inference approach, producing a small subset of nodes that covers the source with pre-specified confidence levels and demonstrating scalability improvements through Monte Carlo strategies.
Diffusion source identification on networks is a problem of fundamental importance in a broad class of applications, including rumor controlling and virus identification. Though this problem has received significant recent attention, most studies have focused only on very restrictive settings and lack theoretical guarantees for more realistic networks. We introduce a statistical framework for the study of diffusion source identification and develop a confidence set inference approach inspired by hypothesis testing. Our method efficiently produces a small subset of nodes, which provably covers the source node with any pre-specified confidence level without restrictive assumptions on network structures. Moreover, we propose multiple Monte Carlo strategies for the inference procedure based on network topology and the probabilistic properties that significantly improve the scalability. To our knowledge, this is the first diffusion source identification method with a practically useful theoretical guarantee on general networks. We demonstrate our approach via extensive synthetic experiments on well-known random network models and a mobility network between cities concerning the COVID-19 spreading.