1. [Publications](/publications)
2. Controlling graph dynamics with reinforcement learning and graph neural networks
 
 # Controlling graph dynamics with reinforcement learning and graph neural networks

  ![Publication image](/sites/default/files/styles/wide/public/default_images/default.jpeg?itok=qUFsuJCP "Publication image")

 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.



 ## Authors



[Eli Meirom](/person/eli-meirom)

[Haggai Maron](/person/haggai-maron)

[Shie Mannor](/person/shie-mannor)

[Gal Chechik](/person/gal-chechik)

 

 

 ## Publication Date



Sunday, June 13, 2021

 

 ## Published in



[ICML 2021](http://proceedings.mlr.press/v139/meirom21a/meirom21a.pdf)

 

 ## Research Area



[Artificial Intelligence and Machine Learning ](/research-area/machine-learning-artificial-intelligence)