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Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization

arXiv 2206.14846 ↗First posted 29 Jun 2022cs.LG cs.SI stat.MLcited by 0

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Abstract (arXiv)

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 O(√T) regret bound for our algorithm. Empirical evaluations on synthetic network demonstrate the efficiency of our algorithm.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 36 references that are in the map. First posted on arXiv 29 Jun 2022.

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References · 14

  1. 2019Provably Efficient Reinforcement Learning with Linear Function Approximationcited by 514
  2. 2020Model-Based Reinforcement Learning with Value-Targeted Regressioncited by 233
  3. 2019Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Boundcited by 199
  4. 2020Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processescited by 199
  5. 2020Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mappingcited by 102
  6. 2017Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applicationscited by 44
  7. 2015Online Influence Maximization (Extended Version)cited by 17
  8. 2020Online Influence Maximization under Linear Threshold Modelcited by 17
  9. 2019Factorization Bandits for Online Influence Maximizationcited by 10
  10. 2015Adaptive Influence Maximization in Dynamic Social Networkscited by 7
  11. 2020Oracle-Efficient Regret Minimization in Factored MDPs with Unknown Structurecited by 6
  12. 2018Online Influence Maximization with Local Observationscited by 3
2 more
  1. 2019Adaptive Influence Maximization with Myopic Feedbackcited by 3
  2. 2019Adaptive Influence Maximization under General Feedback Modelscited by 2

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