Before revision:  In face of an exponential state space, combinatorial action space and partial observability, we design RLGN, a novel tractable Reinforcement Learning (RL) scheme to prioritize which nodes should be tested , using Graph Neural Networks (GNNs) to rank the graph 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. It suggests that prioritizing tests using RL on temporal graphs can increase the number of healthy people by 25 %DIF < and contain the epidemic 30\\% more often than supervised approaches and 2.5\times more often than non-learned baselines using the same resources.abstract 
After revision:  In face of an exponential state space, combinatorial action space and partial observability, we design RLGN, a novel tractable Reinforcement Learning (RL) scheme to prioritize which nodes should be tested  %DIF < and contain the epidemic 30\\% more often than supervised approaches and 2.5\times more often than non-learned baselines using the same resources.abstract