PGSL: A probabilistic graph diffusion model for source localization

Abstract

Source localization, as a reverse problem of the graph diffusion, bears paramount significance for a multitude of applications, such as tracking social rumors, detecting computer viruses, and finding epidemic spreaders. However, the innate uncertainty of the diffusion process complicates this task — different source nodes can result in similar or identical diffusions over time, making the source localization task a complex, ill-posed problem. Most existing solutions utilize deterministic techniques and therefore cannot model the diffusion uncertainty of source nodes. Moreover, current probabilistic approaches are inefficient to conduct smooth transformations with variational inference. To overcome these limitations, we propose a probabilistic framework using normalizing flows with invertible transformations and novel objective optimization methods to explicitly model the uncertainty of the diffusion sources. Moreover, graph neural networks are leveraged to encapsulate propagation patterns between the observed diffusion and sources of high uncertainty. Extensive experiments conducted on six distinct networks demonstrate the effectiveness of our model over strong baselines, up to 11.8% and 8.2% improvements in terms of F1 and AUC, respectively, on Twitter dataset under real-world diffusion.