Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks
Eli Meirom, Haggai Maron, Shie Mannor, Gal Chechik
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.
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.}
}