Online Influence Maximization with Semi-Bandit Feedback under Corruptions
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866913523269894144 |
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| author | Cheng, Xiaotong Nourani-Koliji, Behzad Maghsudi, Setareh |
| author_facet | Cheng, Xiaotong Nourani-Koliji, Behzad Maghsudi, Setareh |
| contents | In this work, we investigate the online influence maximization in social networks. Most prior research studies on online influence maximization assume that the nodes are fully cooperative and act according to their stochastically generated influence probabilities on others. In contrast, we study the online influence maximization problem in the presence of some corrupted nodes whose damaging effects diffuse throughout the network. We propose a novel bandit algorithm, CW-IMLinUCB, which robustly learns and finds the optimal seed set in the presence of corrupted users. Theoretical analyses establish that the regret performance of our proposed algorithm is better than the state-of-the-art online influence maximization algorithms. Extensive empirical evaluations on synthetic and real-world datasets also show the superior performance of our proposed algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_20079 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Online Influence Maximization with Semi-Bandit Feedback under Corruptions Cheng, Xiaotong Nourani-Koliji, Behzad Maghsudi, Setareh Social and Information Networks In this work, we investigate the online influence maximization in social networks. Most prior research studies on online influence maximization assume that the nodes are fully cooperative and act according to their stochastically generated influence probabilities on others. In contrast, we study the online influence maximization problem in the presence of some corrupted nodes whose damaging effects diffuse throughout the network. We propose a novel bandit algorithm, CW-IMLinUCB, which robustly learns and finds the optimal seed set in the presence of corrupted users. Theoretical analyses establish that the regret performance of our proposed algorithm is better than the state-of-the-art online influence maximization algorithms. Extensive empirical evaluations on synthetic and real-world datasets also show the superior performance of our proposed algorithm. |
| title | Online Influence Maximization with Semi-Bandit Feedback under Corruptions |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2409.20079 |