Online Influence Maximization under the Independent Cascade Model with Node-Level Feedback
Abstract
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 -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 -regret algorithm for OIM problem under the IC model with node-level feedback.
Cite
@article{arxiv.2109.06077,
title = {Online Influence Maximization under the Independent Cascade Model with Node-Level Feedback},
author = {Zhijie Zhang and Wei Chen and Xiaoming Sun and Jialin Zhang},
journal= {arXiv preprint arXiv:2109.06077},
year = {2022}
}
Comments
The published version of this paper claims there are OIM algorithms with node-level feedback that use standard offline oracles. However, we later found there are severe bugs in our initial algorithms. In this update, we retrieve most of our results by presenting an OIM algoithm that uses pair oracles