Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks

Eli Meirom, Haggai Maron, Shie Mannor, Gal Chechik
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:7565-7577, 2021.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-meirom21a, title = {Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks}, author = {Meirom, Eli and Maron, Haggai and Mannor, Shie and Chechik, Gal}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {7565--7577}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/meirom21a/meirom21a.pdf}, url = {https://proceedings.mlr.press/v139/meirom21a.html}, 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.} }
Endnote
%0 Conference Paper %T Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks %A Eli Meirom %A Haggai Maron %A Shie Mannor %A Gal Chechik %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-meirom21a %I PMLR %P 7565--7577 %U https://proceedings.mlr.press/v139/meirom21a.html %V 139 %X 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.
APA
Meirom, E., Maron, H., Mannor, S. & Chechik, G.. (2021). Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:7565-7577 Available from https://proceedings.mlr.press/v139/meirom21a.html.

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