Selective network discovery via deep reinforcement learning on embedded spaces

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, ne...

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Bibliographic Details
Published in:Applied network science 2021-03, Vol.6 (1), p.1-20, Article 24
Main Authors: Morales, Peter, Caceres, Rajmonda Sulo, Eliassi-Rad, Tina
Format: Article
Language:English
Subjects:
Algorithms
Apexes
Cognitive tasks
Complexity
Computer Appl. in Social and Behavioral Sciences
Computer Science
Decision making
Deep learning
Downstream effects
Embedding
Incomplete networks
Machine learning
Network embedding
Reinforcement learning
Simulation and Modeling
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Summary: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.
ISSN:2364-8228
2364-8228
DOI:10.1007/s41109-021-00365-8