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Deep Reinforcement Learning for Task-Driven Discovery of Incomplete Networks

  • Peter MoralesEmail author
  • Rajmonda Sulo Caceres
  • Tina Eliassi-Rad
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 881)

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 result, network discovery algorithms optimized for specific downstream learning tasks given resource collection constraints are of great interest. In this paper, we formulate the task-specific network discovery problem in an incomplete network setting as a sequential decision making problem. Our downstream task is selective harvesting, the optimal collection of vertices with a particular attribute. We propose a framework, called Network Actor Critic (NAC), which learns a policy and notion of future reward in an offline setting via a deep reinforcement learning algorithm. A quantitative study is presented on several synthetic and real benchmarks. We show that offline models of reward and network discovery policies lead to significantly improved performance when compared to competitive online discovery algorithms.

Keywords

Network discovery Incomplete networks Deep reinforcement learning 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Peter Morales
    • 1
    Email author
  • Rajmonda Sulo Caceres
    • 1
  • Tina Eliassi-Rad
    • 2
  1. 1.MIT Lincoln LaboratoryLexingtonUSA
  2. 2.Northeastern UniversityBostonUSA

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