text: 222,253,1318592850964205570,1020920111476236288,Haggai Maron,"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 on 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. Joint work with @GalChechik and Shie Mannor Led by Eli Meirom @NVIDIAAI",https://arxiv.org/abs/2010.05313,"We consider the problem of controlling a partially-observed dynamic process on a graph by a limited number of interventions. This problem naturally arises in contexts such as scheduling virus tests to curb an epidemic; targeted marketing in order to promote a product; and manually inspecting posts to detect fake news spreading on social networks. We formulate this setup as a sequential decision problem over a temporal graph process. In face of an exponential state space, combinatorial action space and partial observability, we design a novel tractable scheme to control dynamical processes on temporal graphs. We successfully apply our approach to two popular problems that fall into our framework: prioritizing which nodes should be tested in order to curb the spread of an epidemic, and influence maximization on a graph. ","Controlling Graph Dynamics with Reinforcement Learning and Graph Neural