Finding Patient Zero: Learning Contagion Source with Graph Neural Networks

We are given an interaction graph, whose nodes indicate people, and an edge between two nodes indicates an interaction. We are also given a snapshot, indicating the status of each node as susceptible, infected or recovered. Our goal is to determine the source of the contagion (also known as patient zero). We examine the effectiveness of graph neural networks at this task. We demonstrate the robustness of this method to missing node and edge information. We also examine the effect that the robustness entropy of the interaction graph has on the patient-zero detection accuracy. Check out our poster here.