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We present a novel Graph-based debiasing Algorithm for Underreported Data(GRAUD) aiming at an efficient joint estimation of event counts and discoveryprobabilities across spatial or graphical structures. This innovative methodprovides a solution to problems seen in fields such as policing data andCOVID-$19$ data analysis. Our approach avoids the need for strong priorstypically associated with Bayesian frameworks. By leveraging the graphstructures on unknown variables $n$ and $p$, our method debiases theunder-report data and estimates the discovery probability at the same time. Wevalidate the effectiveness of our method through simulation experiments andillustrate its practicality in one real-world application: police 911calls-to-service data.

A Graph-Prediction-Based Approach for Debiasing Underreported Data