We study the online influence maximization (OIM) problem in social networks, where the learner repeatedly chooses seed nodes to generate cascades, observes the cascade feedback, and gradually learns the best seeds that generate the largest cascade in multiple rounds. In the demand of the real world, we work with node-level feedback instead of the common edge-level feedback in the literature. The edge-level feedback reveals all edges that pass through information in a cascade, whereas the node-level feedback only reveals the activated nodes with timestamps. The node-level feedback is arguably more realistic since in practice it is relatively easy to observe who is influenced but very difficult to observe from which relationship (edge) the influence comes. Previously, there is a nearly optimal $\tilde{O}(\sqrt{T})$-regret algorithm for OIM problem under the linear threshold (LT) diffusion model with node-level feedback. It remains unknown whether the same algorithm exists for the independent cascade (IC) diffusion model. In this paper, we resolve this open problem by presenting an $\tilde{O}(\sqrt{T})$-regret algorithm for OIM problem under the IC model with node-level feedback.
翻译:我们研究社交网络中的在线影响最大化(OIM)问题,学习者反复选择种子节点以产生级联,观察级联反馈,并逐渐学习在多个回合中产生最大级联的最佳种子。在现实世界的需求中,我们使用节点反馈,而不是文献中共同的边缘级反馈。边缘级反馈揭示了在级联中通过信息传递的所有边缘,而节点反馈只显示带有时标的激活节点。节点反馈可以说比较现实,因为在实践中,观察谁受到影响,但很难观察其影响来自哪种关系(边缘)。以前,在线性阈值(LT)传播模型下,我们几乎可以使用美元/tilde{O}(sqrt{T} ) $-regt 算法来应对OIM问题。对于独立级(IC) 传播模型是否存在同样的算法,现在还不清楚。在本文中,我们通过在Itilde{O\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\