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.
After revision:  In face of an exponential state space, combinatorial action space and partial observability, we design RLGN, 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 , using Graph Neural Networks (GNNs) to rank the graph nodes.