Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization. (arXiv:2206.14846v1 [cs.LG])

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 …

arxiv influence learning lg reinforcement reinforcement learning