Jun 29, 2022 · Online influence maximization aims to maximize the influence spread of a content in a social network with unknown network model by selecting a ...
Feb 1, 2023 · We propose a model-based optimistic RL approach to solve the content-dependent online adaptive influence maximization problem.
Workshop: Reinforcement Learning for Real Life (RL4RealLife) Workshop. Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization.
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.
View recent discussion. Abstract: Online influence maximization aims to maximize the influence spread of a content in a social network with unknown network ...
This paper forms the problem as an infinite-horizon discounted MDP under a linear diffusion process and presents a model-based reinforcement learning ...
In this paper, we study the content-dependent online adaptive influence maximization problem: at each round, the agent selects a user-content pair to activate ...
Dec 2, 2022 · Online influence maximization aims to maximize the influence spread of a content in a social network with unknown network model by selecting ...
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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.
2023. Deep Reinforcement Learning for Efficient and Fair Allocation ... Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization.