Budgeted Online Influence Maximization
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917426537431040 |
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| author | Perrault, Pierre Healey, Jennifer Wen, Zheng Valko, Michal |
| author_facet | Perrault, Pierre Healey, Jennifer Wen, Zheng Valko, Michal |
| contents | We introduce a new budgeted framework for online influence maximization, considering the total cost of an advertising campaign instead of the common cardinality constraint on a chosen influencer set. Our approach better models the real-world setting where the cost of influencers varies and advertisers want to find the best value for their overall social advertising budget. We propose an algorithm assuming an independent cascade diffusion model and edge level semi-bandit feedback, and provide both theoretical and experimental results. Our analysis is also valid for the cardinality constraint setting and improves the state of the art regret bound in this case. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_19672 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Budgeted Online Influence Maximization Perrault, Pierre Healey, Jennifer Wen, Zheng Valko, Michal Machine Learning We introduce a new budgeted framework for online influence maximization, considering the total cost of an advertising campaign instead of the common cardinality constraint on a chosen influencer set. Our approach better models the real-world setting where the cost of influencers varies and advertisers want to find the best value for their overall social advertising budget. We propose an algorithm assuming an independent cascade diffusion model and edge level semi-bandit feedback, and provide both theoretical and experimental results. Our analysis is also valid for the cardinality constraint setting and improves the state of the art regret bound in this case. |
| title | Budgeted Online Influence Maximization |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2604.19672 |