Selective network discovery via deep reinforcement learning on embedded spaces
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41109_2021_Article_365.pdf
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3.45 MB
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Adobe PDF
Checksum (MD5)
3161e6e8a27eaecb58c4333011a750ae
Author(s)
Morales, Peter
Caceres, Rajmonda S
Eliassi-Rad, Tina
Date Issued
March 20, 2021
Publisher
Springer International Publishing
Citation
Applied Network Science. 2021 Mar 20;6(1):24
Version
Final published version
Abstract
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 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. The NAC paradigm utilizes a task-specific network embedding to reduce the state space complexity. A detailed comparative analysis of popular network embeddings is presented with respect to their role in supporting offline planning. Furthermore, a quantitative study is presented on various synthetic and real benchmarks using NAC and several baselines. We show that offline models of reward and network discovery policies lead to significantly improved performance when compared to competitive online discovery algorithms. Finally, we outline learning regimes where planning is critical in addressing sparse and changing reward signals.
MIT Department
Lincoln Laboratory
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s41109-021-00365-8
