Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks

• URL: http://arxiv.org/abs/2010.05313v3
• Date: Fri, 9 Jul 2021 06:22:14 GMT
• Title: Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks
• Authors: Eli A. Meirom, Haggai Maron, Shie Mannor, Gal Chechik
• Abstract summary: 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. We formulate this as a decision problem over a temporal graph process.
• Score: 86.05566365115729
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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