On Adaptive Influence Maximization Under General Feedback Models
نویسندگان
چکیده
The classic influence maximization problem explores the strategies for deploying cascades such that total is maximized, and it assumes seed nodes initiate are computed prior to diffusion process. In its adaptive version, allowed be launched in an manner after observing certain results. this article, we provide a systematic study on problem, focusing algorithmic analysis of general feedback models. We introduce concept regret ratio characterize key trade-off designing seeding strategies, based which present approximation well-known greedy policy. addition, concerning improving efficiencies bounding ratio. Finally, propose several future research directions.
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ژورنال
عنوان ژورنال: IEEE Transactions on Emerging Topics in Computing
سال: 2022
ISSN: ['2168-6750', '2376-4562']
DOI: https://doi.org/10.1109/tetc.2020.3031057