Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks

Eli Meirom · Haggai Maron · Shie Mannor · Gal Chechik

[ Abstract ] [ Livestream: Visit Deep Learning Algorithms 2 ] [ Paper ]
[ Paper ]

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.

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