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Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization
Kaixuan Huang · Yu Wu · Xuezhou Zhang · Shenyinying Tu · Qingyun Wu · Mengdi Wang · Huazheng Wang
Online influence maximization aims to maximize the influence spread of a content in a social network with unknown network model by selecting a few seed nodes. Recent studies followed a non-adaptive setting, where the seed nodes are selected before the start of the diffusion process and network parameters are updated when the diffusion stops. We consider an adaptive version of content-dependent online influence maximization problem where the seed nodes are sequentially activated based on real-time feedback. In this paper, we formulate the problem as an infinite-horizon discounted MDP under a linear diffusion process and present a model-based reinforcement learning solution. Our algorithm maintains a network model estimate and selects seed users adaptively, exploring the social network while improving the optimal policy optimistically. We establish $\widetilde \gO(\sqrt{T})$ regret bound for our algorithm. Empirical evaluations on synthetic and real-world networks demonstrate the efficiency of our algorithm.

Author Information

Kaixuan Huang (Princeton University)
Yu Wu (Princeton University)
Xuezhou Zhang (Princeton)
Shenyinying Tu (LinkedIn)
Qingyun Wu (Pennsylvania State University)
Mengdi Wang (Princeton University)

Mengdi Wang is interested in data-driven stochastic optimization and applications in machine and reinforcement learning. She received her PhD in Electrical Engineering and Computer Science from Massachusetts Institute of Technology in 2013. At MIT, Mengdi was affiliated with the Laboratory for Information and Decision Systems and was advised by Dimitri P. Bertsekas. Mengdi became an assistant professor at Princeton in 2014. She received the Young Researcher Prize in Continuous Optimization of the Mathematical Optimization Society in 2016 (awarded once every three years).

Huazheng Wang (Oregon State University)

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