Hyperparametric Robust and Dynamic Influence Maximization
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866913614232813568 |
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| author | Saha, Arkaprava Cautis, Bogdan Xiao, Xiaokui Lakshmanan, Laks V. S. |
| author_facet | Saha, Arkaprava Cautis, Bogdan Xiao, Xiaokui Lakshmanan, Laks V. S. |
| contents | We study the problem of robust influence maximization in dynamic diffusion networks. In line with recent works, we consider the scenario where the network can undergo insertion and removal of nodes and edges, in discrete time steps, and the influence weights are determined by the features of the corresponding nodes and a global hyperparameter. Given this, our goal is to find, at every time step, the seed set maximizing the worst-case influence spread across all possible values of the hyperparameter. We propose an approximate solution using multiplicative weight updates and a greedy algorithm, with provable quality guarantees. Our experiments validate the effectiveness and efficiency of the proposed methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_11827 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hyperparametric Robust and Dynamic Influence Maximization Saha, Arkaprava Cautis, Bogdan Xiao, Xiaokui Lakshmanan, Laks V. S. Databases Social and Information Networks We study the problem of robust influence maximization in dynamic diffusion networks. In line with recent works, we consider the scenario where the network can undergo insertion and removal of nodes and edges, in discrete time steps, and the influence weights are determined by the features of the corresponding nodes and a global hyperparameter. Given this, our goal is to find, at every time step, the seed set maximizing the worst-case influence spread across all possible values of the hyperparameter. We propose an approximate solution using multiplicative weight updates and a greedy algorithm, with provable quality guarantees. Our experiments validate the effectiveness and efficiency of the proposed methods. |
| title | Hyperparametric Robust and Dynamic Influence Maximization |
| topic | Databases Social and Information Networks |
| url | https://arxiv.org/abs/2412.11827 |