Generative–Discriminative Source Localization from Noisy Diffusion Observations

Graph source localization identifies the initial spreaders from node states observed after diffusion. It is an important task in epidemic tracing and information diffusion analysis. One way to assess a source estimate is to compare its predicted diffusion with the observation and refine the estimate by reducing their difference. Under noisy observations, however, we find that a closer fit can leave fewer true sources in the final predicted set. Better diffusion fit therefore does not necessarily improve localization, motivating refinement that directly targets source identification. We propose Generative–Discriminative Source Localization (GDSL). The method first produces an initial source estimate from the observation and network structure. It then predicts the resulting diffusion and computes node-level differences from the observation. A discriminative network combines these differences with the initial source scores and network structure, and learns to adjust node scores using source labels. Diffusion differences thus inform node decisions, while source supervision guides the correction. On SI and SIR simulations over four real-world networks, GDSL achieves the highest Top-K F1 among the evaluated implementations in all eight settings, with mean AUC, AP, and Top-K F1 of 0.963, 0.728, and 0.683. Stage analysis shows that most localization gains arise from discriminative correction, supporting source-label supervision alongside diffusion prediction.