Selective Network Discovery via Deep Reinforcement Learning on Embedded Spaces
This work addresses the challenge of optimizing network discovery for specific learning tasks under resource constraints, which is incremental as it builds on existing reinforcement learning and embedding methods.
The paper tackles the problem of task-specific network discovery in incomplete networks by formulating it as a sequential decision-making problem and proposing the Network Actor Critic (NAC) framework, which uses deep reinforcement learning on network embeddings to learn policies offline, resulting in significantly improved performance over online baselines on synthetic and real benchmarks.
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. 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 several 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.