1 research outputs found
Deep Reinforcement Learning for Task-driven Discovery of Incomplete Networks
Complex networks are often either too large for full exploration, partially
accessible or partially observed. Downstream learning tasks on 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
and 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
vertex classification.We propose a framework, called Network Actor Critic
(NAC), which learns concepts of policy and 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.Comment: Submitted for review to Complex Networks 201