Selective network discovery via deep reinforcement learning on embedded spaces

Selective network discovery via deep reinforcement learning on embedded spaces

Abstract Complex networks are often either too large for full exploration, partially accessible, or partially observed. Downstream learning tasks on these incomplete networks can produce low quality results. In addition, reducing the incompleteness of the network can be costly and nontrivial. As a r...

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Bibliographic Details
Main Authors: Peter Morales, Rajmonda Sulo Caceres, Tina Eliassi-Rad
Format: Article
Language:English
Published: SpringerOpen 2021-03-01
Series:Applied Network Science
Subjects:
Incomplete networks
Reinforcement learning
Network embedding
Online Access:https://doi.org/10.1007/s41109-021-00365-8

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