text:   Networks",5,"['New paper!  “How to Stop Epidemics: Controlling Graph Dynamics with RL and GNNs”. An epidemic is a partially-observed dynamic process that spreads over a temporal contact graph. In this setting, how should we prioritize COVID-19 tests?  <LINK> <LINK>', 'We formulate this problem as a sequential decision problem over a graph. In face of an exponential state space, combinatorial action space, and partial observability, we design RLGN, a tractable RL scheme to prioritize which nodes should be tested, using GNNs to rank the nodes.', 'We evaluate this approach in three types of social networks: community-structured, preferential attachment, and based on statistics from real cellular tracking. RLGN consistently outperforms all baselines in our experiments.', 'We show that prioritizing tests using RLGN\non temporal graphs can increase the number of healthy people by 25% and contain the epidemic 30% more often than supervised approaches and 2.5× more often than non-learned baselines using the same resources.', 'Dynamics matter: Building the contacts map is crucial. The information in the temporal dynamics matters and can help detecting fast moving pockets of the epidemic.\n\nJoint work with @GalChechik and Shie Mannor\nLed by Eli Meirom \n@NVIDIAAI']",20,10,1239