Causal-Inspired Influence Maximization in Hypergraphs Under Temporal Constraints
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8178-6_23
Reference22 articles.
1. Becker, R., D’angelo, G., Ghobadi, S., Gilbert, H.: Fairness in influence maximization through randomization. J. Artif. Intell. Res. 73, 1251–1283 (2022)
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3. Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 57–66 (2001)
4. Erkol, Ş, Mazzilli, D., Radicchi, F.: Effective submodularity of influence maximization on temporal networks. Phys. Rev. E 106(3), 034301 (2022)
5. He, Q., et al.: CAOM: a community-based approach to tackle opinion maximization for social networks. Inf. Sci. 513, 252–269 (2020)
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