Towards Optimal Strategy for Adaptive Probing in Incomplete Networks

We investigate a graph probing problem in which an agent has only an incomplete view G′ ( G of the network and wishes to explore the network with least effort. In each step, the agent selects a node u in G′ to probe. After probing u, the agent gains the information about u and its neighbors. All the neighbors of u become observed and are probable in the subsequent steps (if they have not been probed). What is the best probing strategy to maximize the number of nodes explored in k probes? This problem serves as a fundamental component for other decision-making problems in incomplete networks such as information harvesting in social networks, network crawling, network security, and viral marketing with incomplete information. While there are a few methods proposed for the problem, none can perform consistently well across different network types. In this paper, we establish a strong (in)approximability for the problem, proving that no algorithm can guarantees finite approximation ratio unless P=NP. On the bright side, we design learning frameworks to capture the best probing strategies for individual network. Our extensive experiments suggest that our framework can learn efficient probing strategies that consistently outperform previous heuristics and metric-based approaches.