Denoising Propagation Uncertainty for Information Source Localization

Leyuan Liu, Zhangtao Cheng, Xovee Xu, Fan Zhou

Abstract: Source localization, as a reverse problem of information dissemination on graphs, is crucial for tracking social rumors, detecting computer viruses, and identifying epidemic spreaders. However, existing methods face challenges due to the inherent uncertainty of graph diffusion, as the same diffused observations may start with diverse sources. Furthermore, general graph diffusion models did not consider important properties of the diffusion process. To address these issues, we propose a denoising diffusion probabilistic model (DDPM)-based source localization framework, DDSL. In this framework, we consider two distinct characteristics of information dissemination, namely source prominence and monotone increasing, and present a source localization-oriented invertible graph neural network (GNN). To capture the propagation uncertainties of sources, we design a DDPM-based source generator to generate effective and diverse sources for enhancing model’s robustness. Our experiments demonstrate the effectiveness of the proposed model in improving source localization performance.